diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index 51d21a8d6..8f36dc33e 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -7,8 +7,6 @@ - sections: - local: il_robots title: Imitation Learning for Robots - - local: cameras - title: Cameras - local: bring_your_own_policies title: Bring Your Own Policies - local: integrate_hardware @@ -31,6 +29,8 @@ title: Using the Dataset Tools - local: annotation_tools title: Using the Annotation Tools + - local: dataset_subtask + title: Using Subtasks in the Dataset title: "Datasets" - sections: - local: act @@ -103,11 +103,17 @@ title: Earth Rover Mini - local: omx title: OMX + - local: openarm + title: OpenArm title: "Robots" - sections: - local: phone_teleop title: Phone title: "Teleoperators" +- sections: + - local: cameras + title: Cameras + title: "Sensors" - sections: - local: torch_accelerators title: PyTorch accelerators diff --git a/docs/source/cameras.mdx b/docs/source/cameras.mdx index 5c35be0ba..8af0f5ae5 100644 --- a/docs/source/cameras.mdx +++ b/docs/source/cameras.mdx @@ -1,12 +1,22 @@ # Cameras -LeRobot offers multiple options for video capture, including phone cameras, built-in laptop cameras, external webcams, and Intel RealSense cameras. To efficiently record frames from most cameras, you can use either the `OpenCVCamera` or `RealSenseCamera` class. For additional compatibility details on the `OpenCVCamera` class, refer to the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html). +LeRobot offers multiple options for video capture: -### Finding your camera +| Class | Supported Cameras | +| ----------------- | ----------------------------------- | +| `OpenCVCamera` | Phone, built-in laptop, USB webcams | +| `ZMQCamera` | Network-connected cameras | +| `RealSenseCamera` | Intel RealSense (with depth) | +| `Reachy2Camera` | Reachy 2 robot cameras | -To instantiate a camera, you need a camera identifier. This identifier might change if you reboot your computer or re-plug your camera, a behavior mostly dependant on your operating system. +> [!TIP] +> For `OpenCVCamera` compatibility details, see the [Video I/O with OpenCV Overview](https://docs.opencv.org/4.x/d0/da7/videoio_overview.html). -To find the camera indices of the cameras plugged into your system, run the following script: +### Find your camera + +Every camera requires a unique identifier to be instantiated, allowing you to distinguish between multiple connected devices. + +`OpenCVCamera` and `RealSenseCamera` support auto-discovery. Run the command below to list available devices and their identifiers. Note that these identifiers may change after rebooting your computer or re-plugging the camera, depending on your operating system. ```bash lerobot-find-cameras opencv # or realsense for Intel Realsense cameras @@ -14,7 +24,7 @@ lerobot-find-cameras opencv # or realsense for Intel Realsense cameras The output will look something like this if you have two cameras connected: -``` +```bash --- Detected Cameras --- Camera #0: Name: OpenCV Camera @ 0 @@ -33,13 +43,37 @@ Camera #0: > [!WARNING] > When using Intel RealSense cameras in `macOS`, you could get this [error](https://github.com/IntelRealSense/librealsense/issues/12307): `Error finding RealSense cameras: failed to set power state`, this can be solved by running the same command with `sudo` permissions. Note that using RealSense cameras in `macOS` is unstable. -## Use Cameras +`ZMQCamera` and `Reachy2Camera` do not support auto-discovery. They must be configured manually by providing their network address and port or robot SDK settings. -Below are two examples, demonstrating how to work with the API. +## Use cameras -- **Asynchronous frame capture** using an OpenCV-based camera +### Frame access modes + +All camera classes implement three access modes for capturing frames: + +| Method | Behavior | Blocks? | Best For | +| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------- | ---------------------------------------- | +| `read()` | Waits for the camera hardware to return a frame. May block for a long time depending on the camera and SDK. | Yes | Simple scripts, sequential capture | +| `async_read(timeout_ms)` | Returns the latest unconsumed frame from background thread. Blocks only if buffer is empty, up to `timeout_ms`. Raises `TimeoutError` if no frame arrives. | With a timeout | Control loops synchronized to camera FPS | +| `read_latest(max_age_ms)` | Peeks at the most recent frame in buffer (may be stale). Raises `TimeoutError` if frame is older than `max_age_ms`. | No | UI visualization, logging, monitoring | + +### Usage examples + +The following examples show how to use the camera API to configure and capture frames from different camera types. + +- **Blocking and non-blocking frame capture** using an OpenCV-based camera - **Color and depth capture** using an Intel RealSense camera +> [!WARNING] +> Failing to cleanly disconnect cameras can cause resource leaks. Use the context manager protocol to ensure automatic cleanup: +> +> ```python +> with OpenCVCamera(config) as camera: +> ... +> ``` +> +> You can also call `connect()` and `disconnect()` manually, but always use a `finally` block for the latter. + @@ -60,16 +94,30 @@ config = OpenCVCameraConfig( ) # Instantiate and connect an `OpenCVCamera`, performing a warm-up read (default). -camera = OpenCVCamera(config) -camera.connect() +with OpenCVCamera(config) as camera: + + # Read a frame synchronously — blocks until hardware delivers a new frame + frame = camera.read() + print(f"read() call returned frame with shape:", frame.shape) + + # Read a frame asynchronously with a timeout — returns the latest unconsumed frame or waits up to timeout_ms for a new one + try: + for i in range(10): + frame = camera.async_read(timeout_ms=200) + print(f"async_read call returned frame {i} with shape:", frame.shape) + except TimeoutError as e: + print(f"No frame received within timeout: {e}") + + # Instantly return a frame - returns the most recent frame captured by the camera + try: + initial_frame = camera.read_latest(max_age_ms=1000) + for i in range(10): + frame = camera.read_latest(max_age_ms=1000) + print(f"read_latest call returned frame {i} with shape:", frame.shape) + print(f"Was a new frame received by the camera? {not (initial_frame == frame).any()}") + except TimeoutError as e: + print(f"Frame too old: {e}") -# Read frames asynchronously in a loop via `async_read(timeout_ms)` -try: - for i in range(10): - frame = camera.async_read(timeout_ms=200) - print(f"Async frame {i} shape:", frame.shape) -finally: - camera.disconnect() ``` @@ -111,10 +159,10 @@ finally: -## Use your phone +## Use your phone's camera - + To use your iPhone as a camera on macOS, enable the Continuity Camera feature: @@ -124,83 +172,49 @@ To use your iPhone as a camera on macOS, enable the Continuity Camera feature: For more details, visit [Apple support](https://support.apple.com/en-gb/guide/mac-help/mchl77879b8a/mac). -Your iPhone should be detected automatically when running the camera setup script in the next section. - - + -If you want to use your phone as a camera on Linux, follow these steps to set up a virtual camera +If you want to use your phone as a camera using OBS, follow these steps to set up a virtual camera. -1. _Install `v4l2loopback-dkms` and `v4l-utils`_. Those packages are required to create virtual camera devices (`v4l2loopback`) and verify their settings with the `v4l2-ctl` utility from `v4l-utils`. Install them using: +1. _(Linux only) Install `v4l2loopback-dkms` and `v4l-utils`_. These packages create virtual camera devices and verify their settings. Install with: - -```python +```bash sudo apt install v4l2loopback-dkms v4l-utils ``` - -2. _Install [DroidCam](https://droidcam.app) on your phone_. This app is available for both iOS and Android. -3. _Install [OBS Studio](https://obsproject.com)_. This software will help you manage the camera feed. Install it using [Flatpak](https://flatpak.org): +2. _Install the [DroidCam app](https://droidcam.app) on your phone_. This app is available for both iOS and Android. +3. _Download and install [OBS Studio](https://obsproject.com)_. +4. _Download and install the [DroidCam OBS plugin](https://droidcam.app/obs)_. +5. _Start OBS Studio_. - -```python -flatpak install flathub com.obsproject.Studio -``` - - -4. _Install the DroidCam OBS plugin_. This plugin integrates DroidCam with OBS Studio. Install it with: - - -```python -flatpak install flathub com.obsproject.Studio.Plugin.DroidCam -``` - - -5. _Start OBS Studio_. Launch with: - - -```python -flatpak run com.obsproject.Studio -``` - - -6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480`. -7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it in. +6. _Add your phone as a source_. Follow the instructions [here](https://droidcam.app/obs/usage). Be sure to set the resolution to `640x480` to avoid the watermarks. +7. _Adjust resolution settings_. In OBS Studio, go to `File > Settings > Video` or `OBS > Preferences... > Video`. Change the `Base(Canvas) Resolution` and the `Output(Scaled) Resolution` to `640x480` by manually typing it. 8. _Start virtual camera_. In OBS Studio, follow the instructions [here](https://obsproject.com/kb/virtual-camera-guide). -9. _Verify the virtual camera setup_. Use `v4l2-ctl` to list the devices: +9. _Verify the virtual camera setup and resolution_. + - **Linux**: Use `v4l2-ctl` to list devices and check resolution: + ```bash + v4l2-ctl --list-devices # find VirtualCam and note its /dev/videoX path + v4l2-ctl -d /dev/videoX --get-fmt-video # replace with your VirtualCam path + ``` + You should see `VirtualCam` listed and resolution `640x480`. + - **macOS**: Open Photo Booth or FaceTime and select "OBS Virtual Camera" as the input. + - **Windows**: The native Camera app doesn't support virtual cameras. Use a video conferencing app (Zoom, Teams) or run `lerobot-find-cameras opencv` directly to verify. - -```python -v4l2-ctl --list-devices -``` - +
+Troubleshooting -You should see an entry like: +> The virtual camera resolution is incorrect. -``` -VirtualCam (platform:v4l2loopback-000): -/dev/video1 -``` +Delete the virtual camera source and recreate it. The resolution cannot be changed after creation. -10. _Check the camera resolution_. Use `v4l2-ctl` to ensure that the virtual camera output resolution is `640x480`. Change `/dev/video1` to the port of your virtual camera from the output of `v4l2-ctl --list-devices`. +> Error reading frame in background thread for OpenCVCamera(X): OpenCVCamera(X) frame width=640 or height=480 do not match configured width=1920 or height=1080. - -```python -v4l2-ctl -d /dev/video1 --get-fmt-video -``` - +This error is caused by OBS Virtual Camera advertising a `1920x1080` resolution despite rescaling. The only fix for now is to comment out the width and height check in `_postprocess_image()`. -You should see an entry like: - -``` ->>> Format Video Capture: ->>> Width/Height : 640/480 ->>> Pixel Format : 'YUYV' (YUYV 4:2:2) -``` - -Troubleshooting: If the resolution is not correct you will have to delete the Virtual Camera port and try again as it cannot be changed. - -If everything is set up correctly, you can proceed with the rest of the tutorial. +
 + +If everything is set up correctly, your phone will appear as a standard OpenCV camera and can be used with `OpenCVCamera`. diff --git a/docs/source/dataset_subtask.mdx b/docs/source/dataset_subtask.mdx new file mode 100644 index 000000000..beb5d80bd --- /dev/null +++ b/docs/source/dataset_subtask.mdx @@ -0,0 +1,278 @@ +# Using Subtasks in LeRobot Datasets + +Subtask support in robotics datasets has proven effective in improving robot reasoning and understanding. Subtasks are particularly useful for: + +- **Hierarchical policies**: Building policies that include subtask predictions to visualize robot reasoning in real time +- **Reward modeling**: Helping reward models understand task progression (e.g., SARM-style stage-aware reward models) +- **Task decomposition**: Breaking down complex manipulation tasks into atomic, interpretable steps + +LeRobotDataset now supports subtasks as part of its dataset structure, alongside tasks. + +## What are Subtasks? + +While a **task** describes the overall goal (e.g., "Pick up the apple and place it in the basket"), **subtasks** break down the execution into finer-grained steps: + +1. "Approach the apple" +2. "Grasp the apple" +3. "Lift the apple" +4. "Move to basket" +5. "Release the apple" + +Each frame in the dataset can be annotated with its corresponding subtask, enabling models to learn and predict these intermediate stages. + +An overview of subtask annotation showing how frames are labeled with intermediate subtask stages + +

+ Figure: Overview of subtask annotation. +

+ +**Reference:** _Subtask-learning based for robot self-assembly in flexible collaborative assembly in manufacturing_, Original Article, Published: 19 April 2022. + +## Dataset Structure + +Subtask information is stored in the dataset metadata: + +``` +my-dataset/ +├── data/ +│ └── ... +├── meta/ +│ ├── info.json +│ ├── stats.json +│ ├── tasks.parquet +│ ├── subtasks.parquet # Subtask index → subtask string mapping +│ └── episodes/ +│ └── ... +└── videos/ + └── ... +``` + +### Subtasks Parquet File + +The `meta/subtasks.parquet` file maps subtask indices to their natural language descriptions: + +| subtask_index | subtask (index column) | +| ------------- | ---------------------- | +| 0 | "Approach the apple" | +| 1 | "Grasp the apple" | +| 2 | "Lift the apple" | +| ... | ... | + +### Frame-Level Annotations + +Each frame in the dataset can include a `subtask_index` field that references the subtasks parquet file: + +```python +# Example frame data in the parquet file +{ + "index": 42, + "timestamp": 1.4, + "episode_index": 0, + "task_index": 0, + "subtask_index": 2, # References "Lift the apple" + "observation.state": [...], + "action": [...], +} +``` + +## Annotating Datasets with Subtasks + +We provide a HuggingFace Space for easily annotating any LeRobotDataset with subtasks: + +**[https://huggingface.co/spaces/lerobot/annotate](https://huggingface.co/spaces/lerobot/annotate)** + +After completing your annotation: + +1. Click "Push to Hub" to upload your annotated dataset +2. You can also run the annotation space locally by following the instructions at [github.com/huggingface/lerobot-annotate](https://github.com/huggingface/lerobot-annotate) + +## Loading Datasets with Subtasks + +When you load a dataset with subtask annotations, the subtask information is automatically available: + +```python +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +# Load a dataset with subtask annotations +dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated") + +# Access a sample +sample = dataset[100] + +# The sample includes both task and subtask information +print(sample["task"]) # "Collect the fruit" +print(sample["subtask"]) # "Grasp the apple" +print(sample["task_index"]) # tensor(0) +print(sample["subtask_index"]) # tensor(2) +``` + +### Checking for Subtask Support + +You can check if a dataset has subtask annotations: + +```python +# Check if subtasks are available +has_subtasks = ( + "subtask_index" in dataset.features + and dataset.meta.subtasks is not None +) + +if has_subtasks: + print(f"Dataset has {len(dataset.meta.subtasks)} unique subtasks") + print("Subtasks:", list(dataset.meta.subtasks.index)) +``` + +## Using Subtasks for Training + +### With the Tokenizer Processor + +The `TokenizerProcessor` automatically handles subtask tokenization for Vision-Language Action (VLA) models: + +```python +from lerobot.processor.tokenizer_processor import TokenizerProcessor +from lerobot.processor.pipeline import ProcessorPipeline + +# Create a tokenizer processor +tokenizer_processor = TokenizerProcessor( + tokenizer_name_or_path="google/paligemma-3b-pt-224", + padding="max_length", + max_length=64, +) + +# The processor will automatically tokenize subtasks if present in the batch +# and add them to the observation under: +# - "observation.subtask.tokens" +# - "observation.subtask.attention_mask" +``` + +When subtasks are available in the batch, the tokenizer processor adds: + +- `observation.subtask.tokens`: Tokenized subtask text +- `observation.subtask.attention_mask`: Attention mask for the subtask tokens + +### DataLoader with Subtasks + +```python +import torch +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated") + +dataloader = torch.utils.data.DataLoader( + dataset, + batch_size=16, + shuffle=True, +) + +for batch in dataloader: + # Access subtask information in the batch + subtasks = batch["subtask"] # List of subtask strings + subtask_indices = batch["subtask_index"] # Tensor of subtask indices + + # Use for training hierarchical policies or reward models + print(f"Batch subtasks: {set(subtasks)}") +``` + +## Example Datasets with Subtask Annotations + +Try loading a dataset with subtask annotations: + +```python +from lerobot.datasets.lerobot_dataset import LeRobotDataset + +# Example dataset with subtask annotations +dataset = LeRobotDataset("jadechoghari/collect-fruit-annotated") + +# Explore the subtasks +print("Available subtasks:") +for subtask_name in dataset.meta.subtasks.index: + print(f" - {subtask_name}") + +# Get subtask distribution +subtask_counts = {} +for i in range(len(dataset)): + sample = dataset[i] + subtask = sample["subtask"] + subtask_counts[subtask] = subtask_counts.get(subtask, 0) + 1 + +print("\nSubtask distribution:") +for subtask, count in sorted(subtask_counts.items(), key=lambda x: -x[1]): + print(f" {subtask}: {count} frames") +``` + +## Use Cases + +### 1. Hierarchical Policy Training + +Train policies that predict both actions and current subtask: + +```python +class HierarchicalPolicy(nn.Module): + def __init__(self, num_subtasks): + super().__init__() + self.action_head = nn.Linear(hidden_dim, action_dim) + self.subtask_head = nn.Linear(hidden_dim, num_subtasks) + + def forward(self, observations): + features = self.encoder(observations) + actions = self.action_head(features) + subtask_logits = self.subtask_head(features) + return actions, subtask_logits +``` + +### 2. Stage-Aware Reward Modeling (SARM) + +Build reward models that understand task progression: + +```python +# SARM predicts: +# - Stage: Which subtask is being executed (discrete) +# - Progress: How far along the subtask (continuous 0-1) + +class SARMRewardModel(nn.Module): + def forward(self, observations): + features = self.encoder(observations) + stage_logits = self.stage_classifier(features) + progress = self.progress_regressor(features) + return stage_logits, progress +``` + +### 3. Progress Visualization + +Monitor robot execution by tracking subtask progression: + +```python +def visualize_execution(model, observations): + for t, obs in enumerate(observations): + action, subtask_logits = model(obs) + predicted_subtask = subtask_names[subtask_logits.argmax()] + print(f"t={t}: Executing '{predicted_subtask}'") +``` + +## API Reference + +### LeRobotDataset Properties + +| Property | Type | Description | +| --------------------------- | ---------------------- | ------------------------------------------ | +| `meta.subtasks` | `pd.DataFrame \| None` | DataFrame mapping subtask names to indices | +| `features["subtask_index"]` | `dict` | Feature spec for subtask_index if present | + +### Sample Keys + +When subtasks are available, each sample includes: + +| Key | Type | Description | +| --------------- | -------------- | ------------------------------------ | +| `subtask_index` | `torch.Tensor` | Integer index of the current subtask | +| `subtask` | `str` | Natural language subtask description | + +## Related Resources + +- [SARM Paper](https://arxiv.org/pdf/2509.25358) - Stage-Aware Reward Modeling for Long Horizon Robot Manipulation +- [LeRobot Annotate Space](https://huggingface.co/spaces/lerobot/annotate) - Interactive annotation tool +- [LeRobotDataset v3.0](./lerobot-dataset-v3) - Dataset format documentation diff --git a/docs/source/openarm.mdx b/docs/source/openarm.mdx new file mode 100644 index 000000000..cd4ace912 --- /dev/null +++ b/docs/source/openarm.mdx @@ -0,0 +1,276 @@ +# OpenArm + +[OpenArm](https://openarm.dev) is an open-source 7DOF humanoid arm designed for physical AI research and deployment. + +To get your OpenArm, assembled or DIY, and join the global community, browse verified and certified manufacturers worldwide at [openarm.dev](https://openarm.dev). + +## What's Unique? + +- **Human-Scale Design**: OpenArm is designed with human-like proportions, scaled for a person around 160-165cm tall. This provides an optimal balance between practical reach and manageable inertia for safe, responsive operation. + +- **Safety-First Architecture**: Built with QDD backdrivable motors and high compliance, OpenArm prioritizes safe human-robot interaction while maintaining practical payload capabilities (6.0kg peak / 4.1kg nominal) for real-world tasks. + +- **Built for Durability**: Critical structural components use aluminum and stainless steel construction, ensuring robust performance for repetitive data collection and continuous research use. + +- **Fully Accessible & Buildable**: Every component, from CNC parts and 3D-printed casings to electrical wiring is designed to be purchasable and buildable by individual researchers and labs, with complete fabrication data provided. + +- **Practical & Affordable**: At $6,500 USD for a complete bimanual system, OpenArm delivers research-grade capabilities at a fraction of traditional humanoid robot costs. + +## Platform Requirements + + + **Linux Only**: OpenArm currently only works on Linux. The CAN bus USB adapter + does not have macOS drivers and has not been tested on Windows. + + +## Safety Guide + +Before operating OpenArm, please read the [official safety guide](https://docs.openarm.dev/getting-started/safety-guide). Key points: + +- **Secure installation**: Fasten the arm to a flat, stable surface with screws or clamps +- **Safe distance**: Keep body parts and objects outside the range of motion during operation +- **Protective equipment**: Always wear safety goggles; use additional PPE as needed +- **Payload limits**: Do not exceed specified payload limits (6.0kg peak / 4.1kg nominal per arm) +- **Emergency stop**: Know the location and operation of the emergency stop device +- **Regular inspection**: Check for loose screws, damaged mechanical limits, unusual noises, and wiring damage + +## Hardware Setup + +Follow the official [OpenArm hardware documentation](https://docs.openarm.dev) for: + +- Bill of materials and sourcing +- 3D printing instructions +- Mechanical assembly +- Electrical wiring + +The hardware repositories are available at [github.com/enactic/openarm](https://github.com/enactic/openarm). + +## CAN Bus Setup + +OpenArm uses CAN bus communication with Damiao motors. Once you have the CAN bus USB adapter plugged into your Linux PC, follow the [Damiao Motors and CAN Bus guide](./damiao) to configure the interface. + +Quick setup: + +```bash +# Setup CAN interfaces +lerobot-setup-can --mode=setup --interfaces=can0,can1 + +# Test motor communication +lerobot-setup-can --mode=test --interfaces=can0,can1 +``` + +## Install LeRobot 🤗 + +Follow our [Installation Guide](./installation), then install the Damiao motor support: + +```bash +pip install -e ".[damiao]" +``` + +## Usage + +### Follower Arm (Robot) + + + + +```bash +lerobot-calibrate \ + --robot.type=openarm_follower \ + --robot.port=can0 \ + --robot.side=right \ + --robot.id=my_openarm_follower +``` + + + + +```python +from lerobot.robots.openarm_follower import OpenArmFollower, OpenArmFollowerConfig + +config = OpenArmFollowerConfig( + port="can0", + side="right", # or "left" for left arm + id="my_openarm_follower", +) + +follower = OpenArmFollower(config) +follower.connect() + +# Read current state +obs = follower.get_observation() +print(obs) + +# Send action (position in degrees) +action = { + "joint_1.pos": 0.0, + "joint_2.pos": 0.0, + "joint_3.pos": 0.0, + "joint_4.pos": 45.0, + "joint_5.pos": 0.0, + "joint_6.pos": 0.0, + "joint_7.pos": 0.0, + "gripper.pos": 0.0, +} +follower.send_action(action) + +follower.disconnect() +``` + + + + +### Leader Arm (Teleoperator) + +The leader arm is used for teleoperation - manually moving it to control the follower arm. + + + + +```bash +lerobot-calibrate \ + --teleop.type=openarm_leader \ + --teleop.port=can1 \ + --teleop.id=my_openarm_leader +``` + + + + +```python +from lerobot.teleoperators.openarm_leader import OpenArmLeader, OpenArmLeaderConfig + +config = OpenArmLeaderConfig( + port="can1", + id="my_openarm_leader", + manual_control=True, # Disable torque for manual movement +) + +leader = OpenArmLeader(config) +leader.connect() + +# Read current position (as action to send to follower) +action = leader.get_action() +print(action) + +leader.disconnect() +``` + + + + +### Teleoperation + +To teleoperate OpenArm with leader-follower control: + +```bash +lerobot-teleoperate \ + --robot.type=openarm_follower \ + --robot.port=can0 \ + --robot.side=right \ + --robot.id=my_follower \ + --teleop.type=openarm_leader \ + --teleop.port=can1 \ + --teleop.id=my_leader +``` + +### Bimanual Teleoperation + +To teleoperate a bimanual OpenArm setup with two leader and two follower arms: + +```bash +lerobot-teleoperate \ + --robot.type=bi_openarm_follower \ + --robot.left_arm_config.port=can0 \ + --robot.left_arm_config.side=left \ + --robot.right_arm_config.port=can1 \ + --robot.right_arm_config.side=right \ + --robot.id=my_bimanual_follower \ + --teleop.type=bi_openarm_leader \ + --teleop.left_arm_config.port=can2 \ + --teleop.right_arm_config.port=can3 \ + --teleop.id=my_bimanual_leader +``` + +### Recording Data + +To record a dataset during teleoperation: + +```bash +lerobot-record \ + --robot.type=openarm_follower \ + --robot.port=can0 \ + --robot.side=right \ + --robot.id=my_follower \ + --teleop.type=openarm_leader \ + --teleop.port=can1 \ + --teleop.id=my_leader \ + --repo-id=my_hf_username/my_openarm_dataset \ + --fps=30 \ + --num-episodes=10 +``` + +## Configuration Options + +### Follower Configuration + +| Parameter | Default | Description | +| --------------------- | --------- | ---------------------------------------------------------- | +| `port` | - | CAN interface (e.g., `can0`) | +| `side` | `None` | Arm side: `"left"`, `"right"`, or `None` for custom limits | +| `use_can_fd` | `True` | Enable CAN FD for higher data rates | +| `can_bitrate` | `1000000` | Nominal bitrate (1 Mbps) | +| `can_data_bitrate` | `5000000` | CAN FD data bitrate (5 Mbps) | +| `max_relative_target` | `None` | Safety limit for relative target positions | +| `position_kp` | Per-joint | Position control proportional gains | +| `position_kd` | Per-joint | Position control derivative gains | + +### Leader Configuration + +| Parameter | Default | Description | +| ------------------ | --------- | ----------------------------------- | +| `port` | - | CAN interface (e.g., `can1`) | +| `manual_control` | `True` | Disable torque for manual movement | +| `use_can_fd` | `True` | Enable CAN FD for higher data rates | +| `can_bitrate` | `1000000` | Nominal bitrate (1 Mbps) | +| `can_data_bitrate` | `5000000` | CAN FD data bitrate (5 Mbps) | + +## Motor Configuration + +OpenArm uses Damiao motors with the following default configuration: + +| Joint | Motor Type | Send ID | Recv ID | +| --------------------------- | ---------- | ------- | ------- | +| joint_1 (Shoulder pan) | DM8009 | 0x01 | 0x11 | +| joint_2 (Shoulder lift) | DM8009 | 0x02 | 0x12 | +| joint_3 (Shoulder rotation) | DM4340 | 0x03 | 0x13 | +| joint_4 (Elbow flex) | DM4340 | 0x04 | 0x14 | +| joint_5 (Wrist roll) | DM4310 | 0x05 | 0x15 | +| joint_6 (Wrist pitch) | DM4310 | 0x06 | 0x16 | +| joint_7 (Wrist rotation) | DM4310 | 0x07 | 0x17 | +| gripper | DM4310 | 0x08 | 0x18 | + +## Troubleshooting + +### No Response from Motors + +1. Check power supply connections +2. Verify CAN wiring (CAN-H, CAN-L, GND) +3. Run diagnostics: `lerobot-setup-can --mode=test --interfaces=can0` +4. See the [Damiao troubleshooting guide](./damiao#troubleshooting) for more details + +### CAN Interface Not Found + +Ensure the CAN interface is configured: + +```bash +ip link show can0 +``` + +## Resources + +- [OpenArm Website](https://openarm.dev) +- [OpenArm Documentation](https://docs.openarm.dev) +- [OpenArm GitHub](https://github.com/enactic/openarm) +- [Safety Guide](https://docs.openarm.dev/getting-started/safety-guide) +- [Damiao Motors and CAN Bus](./damiao) diff --git a/docs/source/unitree_g1.mdx b/docs/source/unitree_g1.mdx index e6bffdf1b..ea6bf54ad 100644 --- a/docs/source/unitree_g1.mdx +++ b/docs/source/unitree_g1.mdx @@ -188,7 +188,105 @@ Press `Ctrl+C` to stop the policy. ## Running in Simulation Mode (MuJoCo) -You can now test policies before unleashing them on the physical robot using MuJoCo. To do so simply set `is_simulation=True` in config. +You can test policies before deploying on the physical robot using MuJoCo simulation. Set `is_simulation=True` in config or pass `--robot.is_simulation=true` via CLI. + +### Calibrate Exoskeleton Teleoperator + +```bash +lerobot-calibrate \ + --teleop.type=unitree_g1 \ + --teleop.left_arm_config.port=/dev/ttyACM1 \ + --teleop.right_arm_config.port=/dev/ttyACM0 \ + --teleop.id=exo +``` + +### Teleoperate in Simulation + +```bash +lerobot-teleoperate \ + --robot.type=unitree_g1 \ + --robot.is_simulation=true \ + --teleop.type=unitree_g1 \ + --teleop.left_arm_config.port=/dev/ttyACM1 \ + --teleop.right_arm_config.port=/dev/ttyACM0 \ + --teleop.id=exo \ + --fps=100 +``` + +### Record Dataset in Simulation + +```bash +python -m lerobot.scripts.lerobot_record \ + --robot.type=unitree_g1 \ + --robot.is_simulation=true \ + --robot.cameras='{"global_view": {"type": "zmq", "server_address": "localhost", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \ + --teleop.type=unitree_g1 \ + --teleop.left_arm_config.port=/dev/ttyACM1 \ + --teleop.right_arm_config.port=/dev/ttyACM0 \ + --teleop.id=exo \ + --dataset.repo_id=your-username/dataset-name \ + --dataset.single_task="Test" \ + --dataset.num_episodes=2 \ + --dataset.episode_time_s=5 \ + --dataset.reset_time_s=5 \ + --dataset.push_to_hub=true +``` + +Example simulation dataset: [nepyope/teleop_test_sim](https://huggingface.co/datasets/nepyope/teleop_test_sim) + +--- + +## Running on Real Robot + +Once the robot server is running on the G1 (see Part 3), you can teleoperate and record on the real robot. + +### Start the Camera Server + +On the robot, start the ZMQ image server: + +```bash +python src/lerobot/cameras/zmq/image_server.py +``` + +Keep this running in a separate terminal for camera streaming during recording. + +### Teleoperate Real Robot + +```bash +lerobot-teleoperate \ + --robot.type=unitree_g1 \ + --robot.is_simulation=false \ + --teleop.type=unitree_g1 \ + --teleop.left_arm_config.port=/dev/ttyACM1 \ + --teleop.right_arm_config.port=/dev/ttyACM0 \ + --teleop.id=exo \ + --fps=100 +``` + +### Record Dataset on Real Robot + +```bash +python -m lerobot.scripts.lerobot_record \ + --robot.type=unitree_g1 \ + --robot.is_simulation=false \ + --robot.cameras='{"global_view": {"type": "zmq", "server_address": "172.18.129.215", "port": 5555, "camera_name": "head_camera", "width": 640, "height": 480, "fps": 30}}' \ + --teleop.type=unitree_g1 \ + --teleop.left_arm_config.port=/dev/ttyACM1 \ + --teleop.right_arm_config.port=/dev/ttyACM0 \ + --teleop.id=exo \ + --dataset.repo_id=your-username/dataset-name \ + --dataset.single_task="Test" \ + --dataset.num_episodes=2 \ + --dataset.episode_time_s=5 \ + --dataset.reset_time_s=5 \ + --dataset.push_to_hub=true +``` + +**Note**: Update `server_address` to match your robot's camera server IP. + +Example real robot dataset: [nepyope/teleop_test_real](https://huggingface.co/datasets/nepyope/teleop_test_real) + +--- ## Additional Resources diff --git a/examples/backward_compatibility/replay.py b/examples/backward_compatibility/replay.py index ed78d016f..8de5ba197 100644 --- a/examples/backward_compatibility/replay.py +++ b/examples/backward_compatibility/replay.py @@ -81,24 +81,25 @@ def replay(cfg: ReplayConfig): actions = dataset.hf_dataset.select_columns(ACTION) robot.connect() - log_say("Replaying episode", cfg.play_sounds, blocking=True) - for idx in range(dataset.num_frames): - start_episode_t = time.perf_counter() + try: + log_say("Replaying episode", cfg.play_sounds, blocking=True) + for idx in range(dataset.num_frames): + start_episode_t = time.perf_counter() - action_array = actions[idx][ACTION] - action = {} - for i, name in enumerate(dataset.features[ACTION]["names"]): - key = f"{name.removeprefix('main_')}.pos" - action[key] = action_array[i].item() + action_array = actions[idx][ACTION] + action = {} + for i, name in enumerate(dataset.features[ACTION]["names"]): + key = f"{name.removeprefix('main_')}.pos" + action[key] = action_array[i].item() - action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90) - action["elbow_flex.pos"] -= 90 - robot.send_action(action) + action["shoulder_lift.pos"] = -(action["shoulder_lift.pos"] - 90) + action["elbow_flex.pos"] -= 90 + robot.send_action(action) - dt_s = time.perf_counter() - start_episode_t - precise_sleep(max(1 / dataset.fps - dt_s, 0.0)) - - robot.disconnect() + dt_s = time.perf_counter() - start_episode_t + precise_sleep(max(1 / dataset.fps - dt_s, 0.0)) + finally: + robot.disconnect() if __name__ == "__main__": diff --git a/examples/lekiwi/evaluate.py b/examples/lekiwi/evaluate.py index 2f7f9f95f..a3144a442 100644 --- a/examples/lekiwi/evaluate.py +++ b/examples/lekiwi/evaluate.py @@ -78,40 +78,24 @@ def main(): listener, events = init_keyboard_listener() init_rerun(session_name="lekiwi_evaluate") - if not robot.is_connected: - raise ValueError("Robot is not connected!") + try: + if not robot.is_connected: + raise ValueError("Robot is not connected!") - print("Starting evaluate loop...") - recorded_episodes = 0 - while recorded_episodes < NUM_EPISODES and not events["stop_recording"]: - log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}") + print("Starting evaluate loop...") + recorded_episodes = 0 + while recorded_episodes < NUM_EPISODES and not events["stop_recording"]: + log_say(f"Running inference, recording eval episode {recorded_episodes} of {NUM_EPISODES}") - # Main record loop - record_loop( - robot=robot, - events=events, - fps=FPS, - policy=policy, - preprocessor=preprocessor, # Pass the pre and post policy processors - postprocessor=postprocessor, - dataset=dataset, - control_time_s=EPISODE_TIME_SEC, - single_task=TASK_DESCRIPTION, - display_data=True, - teleop_action_processor=teleop_action_processor, - robot_action_processor=robot_action_processor, - robot_observation_processor=robot_observation_processor, - ) - - # Reset the environment if not stopping or re-recording - if not events["stop_recording"] and ( - (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"] - ): - log_say("Reset the environment") + # Main record loop record_loop( robot=robot, events=events, fps=FPS, + policy=policy, + preprocessor=preprocessor, # Pass the pre and post policy processors + postprocessor=postprocessor, + dataset=dataset, control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, @@ -120,24 +104,42 @@ def main(): robot_observation_processor=robot_observation_processor, ) - if events["rerecord_episode"]: - log_say("Re-record episode") - events["rerecord_episode"] = False - events["exit_early"] = False - dataset.clear_episode_buffer() - continue + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ( + (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"] + ): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, + ) - # Save episode - dataset.save_episode() - recorded_episodes += 1 + if events["rerecord_episode"]: + log_say("Re-record episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue - # Clean up - log_say("Stop recording") - robot.disconnect() - listener.stop() + # Save episode + dataset.save_episode() + recorded_episodes += 1 - dataset.finalize() - dataset.push_to_hub() + finally: + # Clean up + log_say("Stop recording") + robot.disconnect() + listener.stop() + + dataset.finalize() + dataset.push_to_hub() if __name__ == "__main__": diff --git a/examples/lekiwi/record.py b/examples/lekiwi/record.py index 18b9f857e..9292157f7 100644 --- a/examples/lekiwi/record.py +++ b/examples/lekiwi/record.py @@ -74,40 +74,23 @@ def main(): listener, events = init_keyboard_listener() init_rerun(session_name="lekiwi_record") - if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected: - raise ValueError("Robot or teleop is not connected!") + try: + if not robot.is_connected or not leader_arm.is_connected or not keyboard.is_connected: + raise ValueError("Robot or teleop is not connected!") - print("Starting record loop...") - recorded_episodes = 0 - while recorded_episodes < NUM_EPISODES and not events["stop_recording"]: - log_say(f"Recording episode {recorded_episodes}") + print("Starting record loop...") + recorded_episodes = 0 + while recorded_episodes < NUM_EPISODES and not events["stop_recording"]: + log_say(f"Recording episode {recorded_episodes}") - # Main record loop - record_loop( - robot=robot, - events=events, - fps=FPS, - dataset=dataset, - teleop=[leader_arm, keyboard], - control_time_s=EPISODE_TIME_SEC, - single_task=TASK_DESCRIPTION, - display_data=True, - teleop_action_processor=teleop_action_processor, - robot_action_processor=robot_action_processor, - robot_observation_processor=robot_observation_processor, - ) - - # Reset the environment if not stopping or re-recording - if not events["stop_recording"] and ( - (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"] - ): - log_say("Reset the environment") + # Main record loop record_loop( robot=robot, events=events, fps=FPS, + dataset=dataset, teleop=[leader_arm, keyboard], - control_time_s=RESET_TIME_SEC, + control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, teleop_action_processor=teleop_action_processor, @@ -115,26 +98,44 @@ def main(): robot_observation_processor=robot_observation_processor, ) - if events["rerecord_episode"]: - log_say("Re-record episode") - events["rerecord_episode"] = False - events["exit_early"] = False - dataset.clear_episode_buffer() - continue + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ( + (recorded_episodes < NUM_EPISODES - 1) or events["rerecord_episode"] + ): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + teleop=[leader_arm, keyboard], + control_time_s=RESET_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=teleop_action_processor, + robot_action_processor=robot_action_processor, + robot_observation_processor=robot_observation_processor, + ) - # Save episode - dataset.save_episode() - recorded_episodes += 1 + if events["rerecord_episode"]: + log_say("Re-record episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue - # Clean up - log_say("Stop recording") - robot.disconnect() - leader_arm.disconnect() - keyboard.disconnect() - listener.stop() + # Save episode + dataset.save_episode() + recorded_episodes += 1 + finally: + # Clean up + log_say("Stop recording") + robot.disconnect() + leader_arm.disconnect() + keyboard.disconnect() + listener.stop() - dataset.finalize() - dataset.push_to_hub() + dataset.finalize() + dataset.push_to_hub() if __name__ == "__main__": diff --git a/examples/lekiwi/replay.py b/examples/lekiwi/replay.py index 872dacf27..cf89aea16 100644 --- a/examples/lekiwi/replay.py +++ b/examples/lekiwi/replay.py @@ -42,25 +42,27 @@ def main(): # Connect to the robot robot.connect() - if not robot.is_connected: - raise ValueError("Robot is not connected!") + try: + if not robot.is_connected: + raise ValueError("Robot is not connected!") - print("Starting replay loop...") - log_say(f"Replaying episode {EPISODE_IDX}") - for idx in range(len(episode_frames)): - t0 = time.perf_counter() + print("Starting replay loop...") + log_say(f"Replaying episode {EPISODE_IDX}") + for idx in range(len(episode_frames)): + t0 = time.perf_counter() - # Get recorded action from dataset - action = { - name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"]) - } + # Get recorded action from dataset + action = { + name: float(actions[idx][ACTION][i]) + for i, name in enumerate(dataset.features[ACTION]["names"]) + } - # Send action to robot - _ = robot.send_action(action) + # Send action to robot + _ = robot.send_action(action) - precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) - - robot.disconnect() + precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) + finally: + robot.disconnect() if __name__ == "__main__": diff --git a/examples/phone_to_so100/evaluate.py b/examples/phone_to_so100/evaluate.py index 246c923aa..837217eda 100644 --- a/examples/phone_to_so100/evaluate.py +++ b/examples/phone_to_so100/evaluate.py @@ -142,38 +142,24 @@ def main(): listener, events = init_keyboard_listener() init_rerun(session_name="phone_so100_evaluate") - if not robot.is_connected: - raise ValueError("Robot is not connected!") + try: + if not robot.is_connected: + raise ValueError("Robot is not connected!") - print("Starting evaluate loop...") - episode_idx = 0 - for episode_idx in range(NUM_EPISODES): - log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}") + print("Starting evaluate loop...") + episode_idx = 0 + for episode_idx in range(NUM_EPISODES): + log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}") - # Main record loop - record_loop( - robot=robot, - events=events, - fps=FPS, - policy=policy, - preprocessor=preprocessor, # Pass the pre and post policy processors - postprocessor=postprocessor, - dataset=dataset, - control_time_s=EPISODE_TIME_SEC, - single_task=TASK_DESCRIPTION, - display_data=True, - teleop_action_processor=make_default_teleop_action_processor(), - robot_action_processor=robot_ee_to_joints_processor, - robot_observation_processor=robot_joints_to_ee_pose_processor, - ) - - # Reset the environment if not stopping or re-recording - if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]): - log_say("Reset the environment") + # Main record loop record_loop( robot=robot, events=events, fps=FPS, + policy=policy, + preprocessor=preprocessor, # Pass the pre and post policy processors + postprocessor=postprocessor, + dataset=dataset, control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, @@ -182,24 +168,41 @@ def main(): robot_observation_processor=robot_joints_to_ee_pose_processor, ) - if events["rerecord_episode"]: - log_say("Re-record episode") - events["rerecord_episode"] = False - events["exit_early"] = False - dataset.clear_episode_buffer() - continue + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ( + (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"] + ): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=make_default_teleop_action_processor(), + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose_processor, + ) - # Save episode - dataset.save_episode() - episode_idx += 1 + if events["rerecord_episode"]: + log_say("Re-record episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue - # Clean up - log_say("Stop recording") - robot.disconnect() - listener.stop() + # Save episode + dataset.save_episode() + episode_idx += 1 + finally: + # Clean up + log_say("Stop recording") + robot.disconnect() + listener.stop() - dataset.finalize() - dataset.push_to_hub() + dataset.finalize() + dataset.push_to_hub() if __name__ == "__main__": diff --git a/examples/phone_to_so100/record.py b/examples/phone_to_so100/record.py index 7b5b704e2..1f5005db9 100644 --- a/examples/phone_to_so100/record.py +++ b/examples/phone_to_so100/record.py @@ -149,38 +149,23 @@ def main(): listener, events = init_keyboard_listener() init_rerun(session_name="phone_so100_record") - if not robot.is_connected or not phone.is_connected: - raise ValueError("Robot or teleop is not connected!") + try: + if not robot.is_connected or not phone.is_connected: + raise ValueError("Robot or teleop is not connected!") - print("Starting record loop. Move your phone to teleoperate the robot...") - episode_idx = 0 - while episode_idx < NUM_EPISODES and not events["stop_recording"]: - log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}") + print("Starting record loop. Move your phone to teleoperate the robot...") + episode_idx = 0 + while episode_idx < NUM_EPISODES and not events["stop_recording"]: + log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}") - # Main record loop - record_loop( - robot=robot, - events=events, - fps=FPS, - teleop=phone, - dataset=dataset, - control_time_s=EPISODE_TIME_SEC, - single_task=TASK_DESCRIPTION, - display_data=True, - teleop_action_processor=phone_to_robot_ee_pose_processor, - robot_action_processor=robot_ee_to_joints_processor, - robot_observation_processor=robot_joints_to_ee_pose, - ) - - # Reset the environment if not stopping or re-recording - if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]): - log_say("Reset the environment") + # Main record loop record_loop( robot=robot, events=events, fps=FPS, teleop=phone, - control_time_s=RESET_TIME_SEC, + dataset=dataset, + control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, teleop_action_processor=phone_to_robot_ee_pose_processor, @@ -188,25 +173,43 @@ def main(): robot_observation_processor=robot_joints_to_ee_pose, ) - if events["rerecord_episode"]: - log_say("Re-recording episode") - events["rerecord_episode"] = False - events["exit_early"] = False - dataset.clear_episode_buffer() - continue + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ( + episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"] + ): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + teleop=phone, + control_time_s=RESET_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=phone_to_robot_ee_pose_processor, + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose, + ) - # Save episode - dataset.save_episode() - episode_idx += 1 + if events["rerecord_episode"]: + log_say("Re-recording episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue - # Clean up - log_say("Stop recording") - robot.disconnect() - phone.disconnect() - listener.stop() + # Save episode + dataset.save_episode() + episode_idx += 1 + finally: + # Clean up + log_say("Stop recording") + robot.disconnect() + phone.disconnect() + listener.stop() - dataset.finalize() - dataset.push_to_hub() + dataset.finalize() + dataset.push_to_hub() if __name__ == "__main__": diff --git a/examples/phone_to_so100/replay.py b/examples/phone_to_so100/replay.py index 875025dfc..9d7806cf4 100644 --- a/examples/phone_to_so100/replay.py +++ b/examples/phone_to_so100/replay.py @@ -73,32 +73,34 @@ def main(): # Connect to the robot robot.connect() - if not robot.is_connected: - raise ValueError("Robot is not connected!") + try: + if not robot.is_connected: + raise ValueError("Robot is not connected!") - print("Starting replay loop...") - log_say(f"Replaying episode {EPISODE_IDX}") - for idx in range(len(episode_frames)): - t0 = time.perf_counter() + print("Starting replay loop...") + log_say(f"Replaying episode {EPISODE_IDX}") + for idx in range(len(episode_frames)): + t0 = time.perf_counter() - # Get recorded action from dataset - ee_action = { - name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"]) - } + # Get recorded action from dataset + ee_action = { + name: float(actions[idx][ACTION][i]) + for i, name in enumerate(dataset.features[ACTION]["names"]) + } - # Get robot observation - robot_obs = robot.get_observation() + # Get robot observation + robot_obs = robot.get_observation() - # Dataset EE -> robot joints - joint_action = robot_ee_to_joints_processor((ee_action, robot_obs)) + # Dataset EE -> robot joints + joint_action = robot_ee_to_joints_processor((ee_action, robot_obs)) - # Send action to robot - _ = robot.send_action(joint_action) + # Send action to robot + _ = robot.send_action(joint_action) - precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) - - # Clean up - robot.disconnect() + precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) + finally: + # Clean up + robot.disconnect() if __name__ == "__main__": diff --git a/examples/so100_to_so100_EE/evaluate.py b/examples/so100_to_so100_EE/evaluate.py index 87d188f99..b614b89f2 100644 --- a/examples/so100_to_so100_EE/evaluate.py +++ b/examples/so100_to_so100_EE/evaluate.py @@ -142,38 +142,24 @@ def main(): listener, events = init_keyboard_listener() init_rerun(session_name="so100_so100_evaluate") - if not robot.is_connected: - raise ValueError("Robot is not connected!") + try: + if not robot.is_connected: + raise ValueError("Robot is not connected!") - print("Starting evaluate loop...") - episode_idx = 0 - for episode_idx in range(NUM_EPISODES): - log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}") + print("Starting evaluate loop...") + episode_idx = 0 + for episode_idx in range(NUM_EPISODES): + log_say(f"Running inference, recording eval episode {episode_idx + 1} of {NUM_EPISODES}") - # Main record loop - record_loop( - robot=robot, - events=events, - fps=FPS, - policy=policy, - preprocessor=preprocessor, # Pass the pre and post policy processors - postprocessor=postprocessor, - dataset=dataset, - control_time_s=EPISODE_TIME_SEC, - single_task=TASK_DESCRIPTION, - display_data=True, - teleop_action_processor=make_default_teleop_action_processor(), - robot_action_processor=robot_ee_to_joints_processor, - robot_observation_processor=robot_joints_to_ee_pose_processor, - ) - - # Reset the environment if not stopping or re-recording - if not events["stop_recording"] and ((episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"]): - log_say("Reset the environment") + # Main record loop record_loop( robot=robot, events=events, fps=FPS, + policy=policy, + preprocessor=preprocessor, # Pass the pre and post policy processors + postprocessor=postprocessor, + dataset=dataset, control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, @@ -182,24 +168,41 @@ def main(): robot_observation_processor=robot_joints_to_ee_pose_processor, ) - if events["rerecord_episode"]: - log_say("Re-record episode") - events["rerecord_episode"] = False - events["exit_early"] = False - dataset.clear_episode_buffer() - continue + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ( + (episode_idx < NUM_EPISODES - 1) or events["rerecord_episode"] + ): + log_say("Reset the environment") + record_loop( + robot=robot, + events=events, + fps=FPS, + control_time_s=EPISODE_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=make_default_teleop_action_processor(), + robot_action_processor=robot_ee_to_joints_processor, + robot_observation_processor=robot_joints_to_ee_pose_processor, + ) - # Save episode - dataset.save_episode() - episode_idx += 1 + if events["rerecord_episode"]: + log_say("Re-record episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue - # Clean up - log_say("Stop recording") - robot.disconnect() - listener.stop() + # Save episode + dataset.save_episode() + episode_idx += 1 + finally: + # Clean up + log_say("Stop recording") + robot.disconnect() + listener.stop() - dataset.finalize() - dataset.push_to_hub() + dataset.finalize() + dataset.push_to_hub() if __name__ == "__main__": diff --git a/examples/so100_to_so100_EE/record.py b/examples/so100_to_so100_EE/record.py index eead7a9a8..d85a1c5cc 100644 --- a/examples/so100_to_so100_EE/record.py +++ b/examples/so100_to_so100_EE/record.py @@ -146,38 +146,23 @@ def main(): listener, events = init_keyboard_listener() init_rerun(session_name="recording_phone") - if not leader.is_connected or not follower.is_connected: - raise ValueError("Robot or teleop is not connected!") + try: + if not leader.is_connected or not follower.is_connected: + raise ValueError("Robot or teleop is not connected!") - print("Starting record loop...") - episode_idx = 0 - while episode_idx < NUM_EPISODES and not events["stop_recording"]: - log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}") + print("Starting record loop...") + episode_idx = 0 + while episode_idx < NUM_EPISODES and not events["stop_recording"]: + log_say(f"Recording episode {episode_idx + 1} of {NUM_EPISODES}") - # Main record loop - record_loop( - robot=follower, - events=events, - fps=FPS, - teleop=leader, - dataset=dataset, - control_time_s=EPISODE_TIME_SEC, - single_task=TASK_DESCRIPTION, - display_data=True, - teleop_action_processor=leader_joints_to_ee, - robot_action_processor=ee_to_follower_joints, - robot_observation_processor=follower_joints_to_ee, - ) - - # Reset the environment if not stopping or re-recording - if not events["stop_recording"] and (episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"]): - log_say("Reset the environment") + # Main record loop record_loop( robot=follower, events=events, fps=FPS, teleop=leader, - control_time_s=RESET_TIME_SEC, + dataset=dataset, + control_time_s=EPISODE_TIME_SEC, single_task=TASK_DESCRIPTION, display_data=True, teleop_action_processor=leader_joints_to_ee, @@ -185,25 +170,44 @@ def main(): robot_observation_processor=follower_joints_to_ee, ) - if events["rerecord_episode"]: - log_say("Re-recording episode") - events["rerecord_episode"] = False - events["exit_early"] = False - dataset.clear_episode_buffer() - continue + # Reset the environment if not stopping or re-recording + if not events["stop_recording"] and ( + episode_idx < NUM_EPISODES - 1 or events["rerecord_episode"] + ): + log_say("Reset the environment") + record_loop( + robot=follower, + events=events, + fps=FPS, + teleop=leader, + control_time_s=RESET_TIME_SEC, + single_task=TASK_DESCRIPTION, + display_data=True, + teleop_action_processor=leader_joints_to_ee, + robot_action_processor=ee_to_follower_joints, + robot_observation_processor=follower_joints_to_ee, + ) - # Save episode - dataset.save_episode() - episode_idx += 1 + if events["rerecord_episode"]: + log_say("Re-recording episode") + events["rerecord_episode"] = False + events["exit_early"] = False + dataset.clear_episode_buffer() + continue - # Clean up - log_say("Stop recording") - leader.disconnect() - follower.disconnect() - listener.stop() + # Save episode + dataset.save_episode() + episode_idx += 1 - dataset.finalize() - dataset.push_to_hub() + finally: + # Clean up + log_say("Stop recording") + leader.disconnect() + follower.disconnect() + listener.stop() + + dataset.finalize() + dataset.push_to_hub() if __name__ == "__main__": diff --git a/examples/so100_to_so100_EE/replay.py b/examples/so100_to_so100_EE/replay.py index 7d35a7b44..47a2f6635 100644 --- a/examples/so100_to_so100_EE/replay.py +++ b/examples/so100_to_so100_EE/replay.py @@ -74,32 +74,35 @@ def main(): # Connect to the robot robot.connect() - if not robot.is_connected: - raise ValueError("Robot is not connected!") + try: + if not robot.is_connected: + raise ValueError("Robot is not connected!") - print("Starting replay loop...") - log_say(f"Replaying episode {EPISODE_IDX}") - for idx in range(len(episode_frames)): - t0 = time.perf_counter() + print("Starting replay loop...") + log_say(f"Replaying episode {EPISODE_IDX}") + for idx in range(len(episode_frames)): + t0 = time.perf_counter() - # Get recorded action from dataset - ee_action = { - name: float(actions[idx][ACTION][i]) for i, name in enumerate(dataset.features[ACTION]["names"]) - } + # Get recorded action from dataset + ee_action = { + name: float(actions[idx][ACTION][i]) + for i, name in enumerate(dataset.features[ACTION]["names"]) + } - # Get robot observation - robot_obs = robot.get_observation() + # Get robot observation + robot_obs = robot.get_observation() - # Dataset EE -> robot joints - joint_action = robot_ee_to_joints_processor((ee_action, robot_obs)) + # Dataset EE -> robot joints + joint_action = robot_ee_to_joints_processor((ee_action, robot_obs)) - # Send action to robot - _ = robot.send_action(joint_action) + # Send action to robot + _ = robot.send_action(joint_action) - precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) + precise_sleep(max(1.0 / dataset.fps - (time.perf_counter() - t0), 0.0)) - # Clean up - robot.disconnect() + finally: + # Clean up + robot.disconnect() if __name__ == "__main__": diff --git a/pyproject.toml b/pyproject.toml index 27126f855..210d70b6b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -105,12 +105,17 @@ dynamixel = ["dynamixel-sdk>=3.7.31,<3.9.0"] damiao = ["python-can>=4.2.0,<5.0.0"] # Robots +openarms = ["lerobot[damiao]"] gamepad = ["lerobot[pygame-dep]", "hidapi>=0.14.0,<0.15.0"] hopejr = ["lerobot[feetech]", "lerobot[pygame-dep]"] lekiwi = ["lerobot[feetech]", "pyzmq>=26.2.1,<28.0.0"] unitree_g1 = [ "pyzmq>=26.2.1,<28.0.0", - "onnxruntime>=1.16.0,<2.0.0" + "onnxruntime>=1.16.0,<2.0.0", + "pin>=3.0.0,<4.0.0", + "meshcat>=0.3.0,<0.4.0", + "matplotlib>=3.9.0,<4.0.0", + "casadi>=3.6.0,<4.0.0", ] reachy2 = ["reachy2_sdk>=1.0.15,<1.1.0"] kinematics = ["lerobot[placo-dep]"] diff --git a/src/lerobot/cameras/camera.py b/src/lerobot/cameras/camera.py index bfdb571a7..2894e0215 100644 --- a/src/lerobot/cameras/camera.py +++ b/src/lerobot/cameras/camera.py @@ -15,11 +15,12 @@ # limitations under the License. import abc +import warnings from typing import Any from numpy.typing import NDArray # type: ignore # TODO: add type stubs for numpy.typing -from .configs import CameraConfig, ColorMode +from .configs import CameraConfig class Camera(abc.ABC): @@ -30,20 +31,12 @@ class Camera(abc.ABC): Manages basic camera properties (FPS, resolution) and core operations: - Connection/disconnection - - Frame capture (sync/async) + - Frame capture (sync/async/latest) Attributes: fps (int | None): Configured frames per second width (int | None): Frame width in pixels height (int | None): Frame height in pixels - - Example: - class MyCamera(Camera): - def __init__(self, config): ... - @property - def is_connected(self) -> bool: ... - def connect(self, warmup=True): ... - # Plus other required methods """ def __init__(self, config: CameraConfig): @@ -56,6 +49,32 @@ class Camera(abc.ABC): self.width: int | None = config.width self.height: int | None = config.height + def __enter__(self): + """ + Context manager entry. + Automatically connects to the camera. + """ + self.connect() + return self + + def __exit__(self, exc_type, exc_value, traceback) -> None: + """ + Context manager exit. + Automatically disconnects, ensuring resources are released even on error. + """ + self.disconnect() + + def __del__(self) -> None: + """ + Destructor safety net. + Attempts to disconnect if the object is garbage collected without cleanup. + """ + try: + if self.is_connected: + self.disconnect() + except Exception: # nosec B110 + pass + @property @abc.abstractmethod def is_connected(self) -> bool: @@ -89,12 +108,10 @@ class Camera(abc.ABC): pass @abc.abstractmethod - def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]: - """Capture and return a single frame from the camera. + def read(self) -> NDArray[Any]: + """Capture and return a single frame from the camera synchronously. - Args: - color_mode: Desired color mode for the output frame. If None, - uses the camera's default color mode. + This is a blocking call that will wait for the hardware and its SDK. Returns: np.ndarray: Captured frame as a numpy array. @@ -103,17 +120,64 @@ class Camera(abc.ABC): @abc.abstractmethod def async_read(self, timeout_ms: float = ...) -> NDArray[Any]: - """Asynchronously capture and return a single frame from the camera. + """Return the most recent new frame. + + This method retrieves the latest frame captured by the background thread. + If a new frame is already available in the buffer (captured since the last call), + it returns it immediately. + + It blocks up to `timeout_ms` only if the buffer is empty or if the latest frame + was already consumed by a previous `async_read` call. + + Essentially, this method return the latest unconsumed frame, waiting if necessary + for a new one to arrive within the specified timeout. + + Usage: + - Ideal for control loops where you want to ensure every processed frame + is fresh, effectively synchronizing your loop to the camera's FPS. + - Causes of a timeout usually include: very low camera FPS, heavy processing load, + or if the camera is disconnected. Args: - timeout_ms: Maximum time to wait for a frame in milliseconds. - Defaults to implementation-specific timeout. + timeout_ms: Maximum time to wait for a new frame in milliseconds. + Defaults to 200ms (0.2s). Returns: np.ndarray: Captured frame as a numpy array. + + Raises: + TimeoutError: If no new frame arrives within `timeout_ms`. """ pass + def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]: + """Return the most recent frame captured immediately (Peeking). + + This method is non-blocking and returns whatever is currently in the + memory buffer. The frame may be stale, + meaning it could have been captured a while ago (hanging camera scenario e.g.). + + Usage: + Ideal for scenarios requiring zero latency or decoupled frequencies & when + we want a guaranteed frame, such as UI visualization, logging, or + non-critical monitoring. + + Returns: + NDArray[Any]: The frame image (numpy array). + + Raises: + TimeoutError: If the latest frame is older than `max_age_ms`. + NotConnectedError: If the camera is not connected. + RuntimeError: If the camera is connected but has not captured any frames yet. + """ + warnings.warn( + f"{self.__class__.__name__}.read_latest() is not implemented. " + "Please override read_latest(); it will be required in future releases.", + FutureWarning, + stacklevel=2, + ) + return self.async_read() + @abc.abstractmethod def disconnect(self) -> None: """Disconnect from the camera and release resources.""" diff --git a/src/lerobot/cameras/opencv/camera_opencv.py b/src/lerobot/cameras/opencv/camera_opencv.py index b1043ba64..d581e1425 100644 --- a/src/lerobot/cameras/opencv/camera_opencv.py +++ b/src/lerobot/cameras/opencv/camera_opencv.py @@ -70,34 +70,24 @@ class OpenCVCamera(Camera): Example: ```python from lerobot.cameras.opencv import OpenCVCamera - from lerobot.cameras.configuration_opencv import OpenCVCameraConfig, ColorMode, Cv2Rotation + from lerobot.cameras.configuration_opencv import OpenCVCameraConfig # Basic usage with camera index 0 config = OpenCVCameraConfig(index_or_path=0) camera = OpenCVCamera(config) camera.connect() - # Read 1 frame synchronously + # Read 1 frame synchronously (blocking) color_image = camera.read() - print(color_image.shape) - # Read 1 frame asynchronously + # Read 1 frame asynchronously (waits for new frame with a timeout) async_image = camera.async_read() + # Get the latest frame immediately (no wait, returns timestamp) + latest_image, timestamp = camera.read_latest() + # When done, properly disconnect the camera using camera.disconnect() - - # Example with custom settings - custom_config = OpenCVCameraConfig( - index_or_path='/dev/video0', # Or use an index - fps=30, - width=1280, - height=720, - color_mode=ColorMode.RGB, - rotation=Cv2Rotation.ROTATE_90 - ) - custom_camera = OpenCVCamera(custom_config) - # ... connect, read, disconnect ... ``` """ @@ -123,6 +113,7 @@ class OpenCVCamera(Camera): self.stop_event: Event | None = None self.frame_lock: Lock = Lock() self.latest_frame: NDArray[Any] | None = None + self.latest_timestamp: float | None = None self.new_frame_event: Event = Event() self.rotation: int | None = get_cv2_rotation(config.rotation) @@ -146,12 +137,16 @@ class OpenCVCamera(Camera): Connects to the OpenCV camera specified in the configuration. Initializes the OpenCV VideoCapture object, sets desired camera properties - (FPS, width, height), and performs initial checks. + (FPS, width, height), starts the background reading thread and performs initial checks. + + Args: + warmup (bool): If True, waits at connect() time until at least one valid frame + has been captured by the background thread. Defaults to True. Raises: DeviceAlreadyConnectedError: If the camera is already connected. - ConnectionError: If the specified camera index/path is not found or the camera is found but fails to open. - RuntimeError: If the camera opens but fails to apply requested FPS/resolution settings. + ConnectionError: If the specified camera index/path is not found or fails to open. + RuntimeError: If the camera opens but fails to apply requested settings. """ if self.is_connected: raise DeviceAlreadyConnectedError(f"{self} is already connected.") @@ -170,12 +165,16 @@ class OpenCVCamera(Camera): ) self._configure_capture_settings() + self._start_read_thread() - if warmup: + if warmup and self.warmup_s > 0: start_time = time.time() while time.time() - start_time < self.warmup_s: - self.read() + self.async_read(timeout_ms=self.warmup_s * 1000) time.sleep(0.1) + with self.frame_lock: + if self.latest_frame is None: + raise ConnectionError(f"{self} failed to capture frames during warmup.") logger.info(f"{self} connected.") @@ -196,8 +195,7 @@ class OpenCVCamera(Camera): Raises: RuntimeError: If the camera fails to set any of the specified properties to the requested value. - DeviceNotConnectedError: If the camera is not connected when attempting - to configure settings. + DeviceNotConnectedError: If the camera is not connected. """ if not self.is_connected: raise DeviceNotConnectedError(f"Cannot configure settings for {self} as it is not connected.") @@ -339,6 +337,17 @@ class OpenCVCamera(Camera): return found_cameras_info + def _read_from_hardware(self) -> NDArray[Any]: + if self.videocapture is None: + raise DeviceNotConnectedError(f"{self} videocapture is not initialized") + + ret, frame = self.videocapture.read() + + if not ret: + raise RuntimeError(f"{self} read failed (status={ret}).") + + return frame + def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]: """ Reads a single frame synchronously from the camera. @@ -346,11 +355,6 @@ class OpenCVCamera(Camera): This is a blocking call. It waits for the next available frame from the camera hardware via OpenCV. - Args: - color_mode (Optional[ColorMode]): If specified, overrides the default - color mode (`self.color_mode`) for this read operation (e.g., - request RGB even if default is BGR). - Returns: np.ndarray: The captured frame as a NumPy array in the format (height, width, channels), using the specified or default @@ -362,34 +366,34 @@ class OpenCVCamera(Camera): received frame dimensions don't match expectations before rotation. ValueError: If an invalid `color_mode` is requested. """ - if not self.is_connected: - raise DeviceNotConnectedError(f"{self} is not connected.") start_time = time.perf_counter() - if self.videocapture is None: - raise DeviceNotConnectedError(f"{self} videocapture is not initialized") + if color_mode is not None: + logger.warning( + f"{self} read() color_mode parameter is deprecated and will be removed in future versions." + ) - ret, frame = self.videocapture.read() + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") - if not ret or frame is None: - raise RuntimeError(f"{self} read failed (status={ret}).") + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") - processed_frame = self._postprocess_image(frame, color_mode) + self.new_frame_event.clear() + frame = self.async_read(timeout_ms=10000) read_duration_ms = (time.perf_counter() - start_time) * 1e3 logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") - return processed_frame + return frame - def _postprocess_image(self, image: NDArray[Any], color_mode: ColorMode | None = None) -> NDArray[Any]: + def _postprocess_image(self, image: NDArray[Any]) -> NDArray[Any]: """ Applies color conversion, dimension validation, and rotation to a raw frame. Args: image (np.ndarray): The raw image frame (expected BGR format from OpenCV). - color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None, - uses the instance's default `self.color_mode`. Returns: np.ndarray: The processed image frame. @@ -399,11 +403,10 @@ class OpenCVCamera(Camera): RuntimeError: If the raw frame dimensions do not match the configured `width` and `height`. """ - requested_color_mode = self.color_mode if color_mode is None else color_mode - if requested_color_mode not in (ColorMode.RGB, ColorMode.BGR): + if self.color_mode not in (ColorMode.RGB, ColorMode.BGR): raise ValueError( - f"Invalid color mode '{requested_color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}." + f"Invalid color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}." ) h, w, c = image.shape @@ -417,7 +420,7 @@ class OpenCVCamera(Camera): raise RuntimeError(f"{self} frame channels={c} do not match expected 3 channels (RGB/BGR).") processed_image = image - if requested_color_mode == ColorMode.RGB: + if self.color_mode == ColorMode.RGB: processed_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if self.rotation in [cv2.ROTATE_90_CLOCKWISE, cv2.ROTATE_90_COUNTERCLOCKWISE, cv2.ROTATE_180]: @@ -431,7 +434,7 @@ class OpenCVCamera(Camera): On each iteration: 1. Reads a color frame - 2. Stores result in latest_frame (thread-safe) + 2. Stores result in latest_frame and updates timestamp (thread-safe) 3. Sets new_frame_event to notify listeners Stops on DeviceNotConnectedError, logs other errors and continues. @@ -439,30 +442,37 @@ class OpenCVCamera(Camera): if self.stop_event is None: raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.") + failure_count = 0 while not self.stop_event.is_set(): try: - color_image = self.read() + raw_frame = self._read_from_hardware() + processed_frame = self._postprocess_image(raw_frame) + capture_time = time.perf_counter() with self.frame_lock: - self.latest_frame = color_image + self.latest_frame = processed_frame + self.latest_timestamp = capture_time self.new_frame_event.set() + failure_count = 0 except DeviceNotConnectedError: break except Exception as e: - logger.warning(f"Error reading frame in background thread for {self}: {e}") + if failure_count <= 10: + failure_count += 1 + logger.warning(f"Error reading frame in background thread for {self}: {e}") + else: + raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e def _start_read_thread(self) -> None: """Starts or restarts the background read thread if it's not running.""" - if self.thread is not None and self.thread.is_alive(): - self.thread.join(timeout=0.1) - if self.stop_event is not None: - self.stop_event.set() + self._stop_read_thread() self.stop_event = Event() self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop") self.thread.daemon = True self.thread.start() + time.sleep(0.1) def _stop_read_thread(self) -> None: """Signals the background read thread to stop and waits for it to join.""" @@ -475,6 +485,11 @@ class OpenCVCamera(Camera): self.thread = None self.stop_event = None + with self.frame_lock: + self.latest_frame = None + self.latest_timestamp = None + self.new_frame_event.clear() + def async_read(self, timeout_ms: float = 200) -> NDArray[Any]: """ Reads the latest available frame asynchronously. @@ -482,6 +497,7 @@ class OpenCVCamera(Camera): This method retrieves the most recent frame captured by the background read thread. It does not block waiting for the camera hardware directly, but may wait up to timeout_ms for the background thread to provide a frame. + It is “best effort” under high FPS. Args: timeout_ms (float): Maximum time in milliseconds to wait for a frame @@ -500,13 +516,12 @@ class OpenCVCamera(Camera): raise DeviceNotConnectedError(f"{self} is not connected.") if self.thread is None or not self.thread.is_alive(): - self._start_read_thread() + raise RuntimeError(f"{self} read thread is not running.") if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0): - thread_alive = self.thread is not None and self.thread.is_alive() raise TimeoutError( f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. " - f"Read thread alive: {thread_alive}." + f"Read thread alive: {self.thread.is_alive()}." ) with self.frame_lock: @@ -518,6 +533,42 @@ class OpenCVCamera(Camera): return frame + def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]: + """Return the most recent frame captured immediately (Peeking). + + This method is non-blocking and returns whatever is currently in the + memory buffer. The frame may be stale, + meaning it could have been captured a while ago (hanging camera scenario e.g.). + + Returns: + NDArray[Any]: The frame image (numpy array). + + Raises: + TimeoutError: If the latest frame is older than `max_age_ms`. + DeviceNotConnectedError: If the camera is not connected. + RuntimeError: If the camera is connected but has not captured any frames yet. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") + + with self.frame_lock: + frame = self.latest_frame + timestamp = self.latest_timestamp + + if frame is None or timestamp is None: + raise RuntimeError(f"{self} has not captured any frames yet.") + + age_ms = (time.perf_counter() - timestamp) * 1e3 + if age_ms > max_age_ms: + raise TimeoutError( + f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)." + ) + + return frame + def disconnect(self) -> None: """ Disconnects from the camera and cleans up resources. @@ -538,4 +589,9 @@ class OpenCVCamera(Camera): self.videocapture.release() self.videocapture = None + with self.frame_lock: + self.latest_frame = None + self.latest_timestamp = None + self.new_frame_event.clear() + logger.info(f"{self} disconnected.") diff --git a/src/lerobot/cameras/reachy2_camera/reachy2_camera.py b/src/lerobot/cameras/reachy2_camera/reachy2_camera.py index c8916c5ee..5cede466d 100644 --- a/src/lerobot/cameras/reachy2_camera/reachy2_camera.py +++ b/src/lerobot/cameras/reachy2_camera/reachy2_camera.py @@ -80,6 +80,8 @@ class Reachy2Camera(Camera): self.config = config self.color_mode = config.color_mode + self.latest_frame: NDArray[Any] | None = None + self.latest_timestamp: float | None = None self.cam_manager: CameraManager | None = None @@ -125,12 +127,7 @@ class Reachy2Camera(Camera): """ Reads a single frame synchronously from the camera. - This is a blocking call. - - Args: - color_mode (Optional[ColorMode]): If specified, overrides the default - color mode (`self.color_mode`) for this read operation (e.g., - request RGB even if default is BGR). + This method retrieves the most recent frame available in Reachy 2's low-level software. Returns: np.ndarray: The captured frame as a NumPy array in the format @@ -145,6 +142,11 @@ class Reachy2Camera(Camera): if self.cam_manager is None: raise DeviceNotConnectedError(f"{self} is not connected.") + if color_mode is not None: + logger.warning( + f"{self} read() color_mode parameter is deprecated and will be removed in future versions." + ) + frame: NDArray[Any] = np.empty((0, 0, 3), dtype=np.uint8) if self.config.name == "teleop" and hasattr(self.cam_manager, "teleop"): @@ -165,11 +167,18 @@ class Reachy2Camera(Camera): raise ValueError(f"Invalid camera name '{self.config.name}'. Expected 'teleop' or 'depth'.") if frame is None: - return np.empty((0, 0, 3), dtype=np.uint8) + raise RuntimeError(f"Internal error: No frame available for {self}.") - if self.config.color_mode == "rgb": + if self.color_mode not in (ColorMode.RGB, ColorMode.BGR): + raise ValueError( + f"Invalid color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}." + ) + if self.color_mode == ColorMode.RGB: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + self.latest_frame = frame + self.latest_timestamp = time.perf_counter() + read_duration_ms = (time.perf_counter() - start_time) * 1e3 logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") @@ -177,13 +186,7 @@ class Reachy2Camera(Camera): def async_read(self, timeout_ms: float = 200) -> NDArray[Any]: """ - Reads the latest available frame. - - This method retrieves the most recent frame available in Reachy 2's low-level software. - - Args: - timeout_ms (float): Maximum time in milliseconds to wait for a frame - to become available. Defaults to 200ms (0.2 seconds). + Same as read() Returns: np.ndarray: The latest captured frame as a NumPy array in the format @@ -197,12 +200,38 @@ class Reachy2Camera(Camera): if not self.is_connected: raise DeviceNotConnectedError(f"{self} is not connected.") - frame = self.read() + return self.read() - if frame is None: - raise RuntimeError(f"Internal error: No frame available for {self}.") + def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]: + """Return the most recent frame captured immediately (Peeking). - return frame + This method is non-blocking and returns whatever is currently in the + memory buffer. The frame may be stale, + meaning it could have been captured a while ago (hanging camera scenario e.g.). + + Returns: + tuple[NDArray, float]: + - The frame image (numpy array). + - The timestamp (time.perf_counter) when this frame was captured. + + Raises: + TimeoutError: If the latest frame is older than `max_age_ms`. + DeviceNotConnectedError: If the camera is not connected. + RuntimeError: If the camera is connected but has not captured any frames yet. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + if self.latest_frame is None or self.latest_timestamp is None: + raise RuntimeError(f"{self} has not captured any frames yet.") + + age_ms = (time.perf_counter() - self.latest_timestamp) * 1e3 + if age_ms > max_age_ms: + raise TimeoutError( + f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)." + ) + + return self.latest_frame def disconnect(self) -> None: """ diff --git a/src/lerobot/cameras/realsense/camera_realsense.py b/src/lerobot/cameras/realsense/camera_realsense.py index f2906fdd8..e47f25381 100644 --- a/src/lerobot/cameras/realsense/camera_realsense.py +++ b/src/lerobot/cameras/realsense/camera_realsense.py @@ -72,15 +72,14 @@ class RealSenseCamera(Camera): camera = RealSenseCamera(config) camera.connect() - # Read 1 frame synchronously + # Read 1 frame synchronously (blocking) color_image = camera.read() - print(color_image.shape) - # Read 1 frame asynchronously + # Read 1 frame asynchronously (waits for new frame with a timeout) async_image = camera.async_read() - # When done, properly disconnect the camera using - camera.disconnect() + # Get the latest frame immediately (no wait, returns timestamp) + latest_image, timestamp = camera.read_latest() # Example with depth capture and custom settings custom_config = RealSenseCameraConfig( @@ -133,7 +132,9 @@ class RealSenseCamera(Camera): self.thread: Thread | None = None self.stop_event: Event | None = None self.frame_lock: Lock = Lock() - self.latest_frame: NDArray[Any] | None = None + self.latest_color_frame: NDArray[Any] | None = None + self.latest_depth_frame: NDArray[Any] | None = None + self.latest_timestamp: float | None = None self.new_frame_event: Event = Event() self.rotation: int | None = get_cv2_rotation(config.rotation) @@ -158,6 +159,10 @@ class RealSenseCamera(Camera): Initializes the RealSense pipeline, configures the required streams (color and optionally depth), starts the pipeline, and validates the actual stream settings. + Args: + warmup (bool): If True, waits at connect() time until at least one valid frame + has been captured by the background thread. Defaults to True. + Raises: DeviceAlreadyConnectedError: If the camera is already connected. ValueError: If the configuration is invalid (e.g., missing serial/name, name not unique). @@ -181,15 +186,18 @@ class RealSenseCamera(Camera): ) from e self._configure_capture_settings() + self._start_read_thread() - if warmup: - time.sleep( - 1 - ) # NOTE(Steven): RS cameras need a bit of time to warm up before the first read. If we don't wait, the first read from the warmup will raise. - start_time = time.time() - while time.time() - start_time < self.warmup_s: - self.read() - time.sleep(0.1) + # NOTE(Steven/Caroline): Enforcing at least one second of warmup as RS cameras need a bit of time before the first read. If we don't wait, the first read from the warmup will raise. + self.warmup_s = max(self.warmup_s, 1) + + start_time = time.time() + while time.time() - start_time < self.warmup_s: + self.async_read(timeout_ms=self.warmup_s * 1000) + time.sleep(0.1) + with self.frame_lock: + if self.latest_color_frame is None or self.use_depth and self.latest_depth_frame is None: + raise ConnectionError(f"{self} failed to capture frames during warmup.") logger.info(f"{self} connected.") @@ -319,9 +327,6 @@ class RealSenseCamera(Camera): This is a blocking call. It waits for a coherent set of frames (depth) from the camera hardware via the RealSense pipeline. - Args: - timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms. - Returns: np.ndarray: The depth map as a NumPy array (height, width) of type `np.uint16` (raw depth values in millimeters) and rotation. @@ -330,44 +335,52 @@ class RealSenseCamera(Camera): DeviceNotConnectedError: If the camera is not connected. RuntimeError: If reading frames from the pipeline fails or frames are invalid. """ + if timeout_ms: + logger.warning( + f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions." + ) - if not self.is_connected: - raise DeviceNotConnectedError(f"{self} is not connected.") if not self.use_depth: raise RuntimeError( f"Failed to capture depth frame '.read_depth()'. Depth stream is not enabled for {self}." ) - start_time = time.perf_counter() + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") + + self.new_frame_event.clear() + + _ = self.async_read(timeout_ms=10000) + + with self.frame_lock: + depth_map = self.latest_depth_frame + + if depth_map is None: + raise RuntimeError("No depth frame available. Ensure camera is streaming.") + + return depth_map + + def _read_from_hardware(self): if self.rs_pipeline is None: raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.") - ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms) + ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=10000) if not ret or frame is None: - raise RuntimeError(f"{self} read_depth failed (status={ret}).") + raise RuntimeError(f"{self} read failed (status={ret}).") - depth_frame = frame.get_depth_frame() - depth_map = np.asanyarray(depth_frame.get_data()) + return frame - depth_map_processed = self._postprocess_image(depth_map, depth_frame=True) - - read_duration_ms = (time.perf_counter() - start_time) * 1e3 - logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") - - return depth_map_processed - - def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 200) -> NDArray[Any]: + def read(self, color_mode: ColorMode | None = None, timeout_ms: int = 0) -> NDArray[Any]: """ Reads a single frame (color) synchronously from the camera. This is a blocking call. It waits for a coherent set of frames (color) from the camera hardware via the RealSense pipeline. - Args: - timeout_ms (int): Maximum time in milliseconds to wait for a frame. Defaults to 200ms. - Returns: np.ndarray: The captured color frame as a NumPy array (height, width, channels), processed according to `color_mode` and rotation. @@ -378,39 +391,39 @@ class RealSenseCamera(Camera): ValueError: If an invalid `color_mode` is requested. """ + start_time = time.perf_counter() + + if color_mode is not None: + logger.warning( + f"{self} read() color_mode parameter is deprecated and will be removed in future versions." + ) + + if timeout_ms: + logger.warning( + f"{self} read() timeout_ms parameter is deprecated and will be removed in future versions." + ) + if not self.is_connected: raise DeviceNotConnectedError(f"{self} is not connected.") - start_time = time.perf_counter() + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") - if self.rs_pipeline is None: - raise RuntimeError(f"{self}: rs_pipeline must be initialized before use.") + self.new_frame_event.clear() - ret, frame = self.rs_pipeline.try_wait_for_frames(timeout_ms=timeout_ms) - - if not ret or frame is None: - raise RuntimeError(f"{self} read failed (status={ret}).") - - color_frame = frame.get_color_frame() - color_image_raw = np.asanyarray(color_frame.get_data()) - - color_image_processed = self._postprocess_image(color_image_raw, color_mode) + frame = self.async_read(timeout_ms=10000) read_duration_ms = (time.perf_counter() - start_time) * 1e3 logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") - return color_image_processed + return frame - def _postprocess_image( - self, image: NDArray[Any], color_mode: ColorMode | None = None, depth_frame: bool = False - ) -> NDArray[Any]: + def _postprocess_image(self, image: NDArray[Any], depth_frame: bool = False) -> NDArray[Any]: """ Applies color conversion, dimension validation, and rotation to a raw color frame. Args: image (np.ndarray): The raw image frame (expected RGB format from RealSense). - color_mode (Optional[ColorMode]): The target color mode (RGB or BGR). If None, - uses the instance's default `self.color_mode`. Returns: np.ndarray: The processed image frame according to `self.color_mode` and `self.rotation`. @@ -421,9 +434,9 @@ class RealSenseCamera(Camera): `width` and `height`. """ - if color_mode and color_mode not in (ColorMode.RGB, ColorMode.BGR): + if self.color_mode and self.color_mode not in (ColorMode.RGB, ColorMode.BGR): raise ValueError( - f"Invalid requested color mode '{color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}." + f"Invalid requested color mode '{self.color_mode}'. Expected {ColorMode.RGB} or {ColorMode.BGR}." ) if depth_frame: @@ -454,7 +467,7 @@ class RealSenseCamera(Camera): On each iteration: 1. Reads a color frame with 500ms timeout - 2. Stores result in latest_frame (thread-safe) + 2. Stores result in latest_frame and updates timestamp (thread-safe) 3. Sets new_frame_event to notify listeners Stops on DeviceNotConnectedError, logs other errors and continues. @@ -462,25 +475,41 @@ class RealSenseCamera(Camera): if self.stop_event is None: raise RuntimeError(f"{self}: stop_event is not initialized before starting read loop.") + failure_count = 0 while not self.stop_event.is_set(): try: - color_image = self.read(timeout_ms=500) + frame = self._read_from_hardware() + color_frame_raw = frame.get_color_frame() + color_frame = np.asanyarray(color_frame_raw.get_data()) + processed_color_frame = self._postprocess_image(color_frame) + + if self.use_depth: + depth_frame_raw = frame.get_depth_frame() + depth_frame = np.asanyarray(depth_frame_raw.get_data()) + processed_depth_frame = self._postprocess_image(depth_frame, depth_frame=True) + + capture_time = time.perf_counter() with self.frame_lock: - self.latest_frame = color_image + self.latest_color_frame = processed_color_frame + if self.use_depth: + self.latest_depth_frame = processed_depth_frame + self.latest_timestamp = capture_time self.new_frame_event.set() + failure_count = 0 except DeviceNotConnectedError: break except Exception as e: - logger.warning(f"Error reading frame in background thread for {self}: {e}") + if failure_count <= 10: + failure_count += 1 + logger.warning(f"Error reading frame in background thread for {self}: {e}") + else: + raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e def _start_read_thread(self) -> None: """Starts or restarts the background read thread if it's not running.""" - if self.thread is not None and self.thread.is_alive(): - self.thread.join(timeout=0.1) - if self.stop_event is not None: - self.stop_event.set() + self._stop_read_thread() self.stop_event = Event() self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop") @@ -498,6 +527,12 @@ class RealSenseCamera(Camera): self.thread = None self.stop_event = None + with self.frame_lock: + self.latest_color_frame = None + self.latest_depth_frame = None + self.latest_timestamp = None + self.new_frame_event.clear() + # NOTE(Steven): Missing implementation for depth for now def async_read(self, timeout_ms: float = 200) -> NDArray[Any]: """ @@ -506,6 +541,7 @@ class RealSenseCamera(Camera): This method retrieves the most recent color frame captured by the background read thread. It does not block waiting for the camera hardware directly, but may wait up to timeout_ms for the background thread to provide a frame. + It is “best effort” under high FPS. Args: timeout_ms (float): Maximum time in milliseconds to wait for a frame @@ -524,17 +560,16 @@ class RealSenseCamera(Camera): raise DeviceNotConnectedError(f"{self} is not connected.") if self.thread is None or not self.thread.is_alive(): - self._start_read_thread() + raise RuntimeError(f"{self} read thread is not running.") if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0): - thread_alive = self.thread is not None and self.thread.is_alive() raise TimeoutError( f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. " - f"Read thread alive: {thread_alive}." + f"Read thread alive: {self.thread.is_alive()}." ) with self.frame_lock: - frame = self.latest_frame + frame = self.latest_color_frame self.new_frame_event.clear() if frame is None: @@ -542,6 +577,43 @@ class RealSenseCamera(Camera): return frame + # NOTE(Steven): Missing implementation for depth for now + def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]: + """Return the most recent (color) frame captured immediately (Peeking). + + This method is non-blocking and returns whatever is currently in the + memory buffer. The frame may be stale, + meaning it could have been captured a while ago (hanging camera scenario e.g.). + + Returns: + NDArray[Any]: The frame image (numpy array). + + Raises: + TimeoutError: If the latest frame is older than `max_age_ms`. + DeviceNotConnectedError: If the camera is not connected. + RuntimeError: If the camera is connected but has not captured any frames yet. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") + + with self.frame_lock: + frame = self.latest_color_frame + timestamp = self.latest_timestamp + + if frame is None or timestamp is None: + raise RuntimeError(f"{self} has not captured any frames yet.") + + age_ms = (time.perf_counter() - timestamp) * 1e3 + if age_ms > max_age_ms: + raise TimeoutError( + f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)." + ) + + return frame + def disconnect(self) -> None: """ Disconnects from the camera, stops the pipeline, and cleans up resources. @@ -565,4 +637,10 @@ class RealSenseCamera(Camera): self.rs_pipeline = None self.rs_profile = None + with self.frame_lock: + self.latest_color_frame = None + self.latest_depth_frame = None + self.latest_timestamp = None + self.new_frame_event.clear() + logger.info(f"{self} disconnected.") diff --git a/src/lerobot/cameras/zmq/camera_zmq.py b/src/lerobot/cameras/zmq/camera_zmq.py index 1a4155f4b..f29e16a28 100644 --- a/src/lerobot/cameras/zmq/camera_zmq.py +++ b/src/lerobot/cameras/zmq/camera_zmq.py @@ -45,6 +45,12 @@ logger = logging.getLogger(__name__) class ZMQCamera(Camera): """ + Manages camera interactions via ZeroMQ for receiving frames from a remote server. + + This class connects to a ZMQ Publisher, subscribes to frame topics, and decodes + incoming JSON messages containing Base64 encoded images. It supports both + synchronous and asynchronous frame reading patterns. + Example usage: ```python from lerobot.cameras.zmq import ZMQCamera, ZMQCameraConfig @@ -52,7 +58,16 @@ class ZMQCamera(Camera): config = ZMQCameraConfig(server_address="192.168.123.164", port=5555, camera_name="head_camera") camera = ZMQCamera(config) camera.connect() - frame = camera.read() + + # Read 1 frame synchronously (blocking) + color_image = camera.read() + + # Read 1 frame asynchronously (waits for new frame with a timeout) + async_image = camera.async_read() + + # Get the latest frame immediately (no wait, returns timestamp) + latest_image, timestamp = camera.read_latest() + camera.disconnect() ``` """ @@ -68,14 +83,17 @@ class ZMQCamera(Camera): self.color_mode = config.color_mode self.timeout_ms = config.timeout_ms + # ZMQ Context and Socket self.context: zmq.Context | None = None self.socket: zmq.Socket | None = None self._connected = False + # Threading resources self.thread: Thread | None = None self.stop_event: Event | None = None self.frame_lock: Lock = Lock() self.latest_frame: NDArray[Any] | None = None + self.latest_timestamp: float | None = None self.new_frame_event: Event = Event() def __str__(self) -> str: @@ -83,10 +101,16 @@ class ZMQCamera(Camera): @property def is_connected(self) -> bool: + """Checks if the ZMQ socket is initialized and connected.""" return self._connected and self.context is not None and self.socket is not None def connect(self, warmup: bool = True) -> None: - """Connect to ZMQ camera server.""" + """Connect to ZMQ camera server. + + Args: + warmup (bool): If True, waits for the camera to provide at least one + valid frame before returning. Defaults to True. + """ if self.is_connected: raise DeviceAlreadyConnectedError(f"{self} is already connected.") @@ -103,17 +127,28 @@ class ZMQCamera(Camera): self.socket.connect(f"tcp://{self.server_address}:{self.port}") self._connected = True - # Auto-detect resolution + # Auto-detect resolution if not provided if self.width is None or self.height is None: - h, w = self.read().shape[:2] + # Read directly from hardware because the thread isn't running yet + temp_frame = self._read_from_hardware() + h, w = temp_frame.shape[:2] self.height = h self.width = w - logger.info(f"{self} resolution: {w}x{h}") + logger.info(f"{self} resolution detected: {w}x{h}") + self._start_read_thread() logger.info(f"{self} connected.") if warmup: - time.sleep(0.1) + # Ensure we have captured at least one frame via the thread + start_time = time.time() + while time.time() - start_time < (self.config.warmup_s): # Wait a bit more than timeout + self.async_read(timeout_ms=self.config.warmup_s * 1000) + time.sleep(0.1) + + with self.frame_lock: + if self.latest_frame is None: + raise ConnectionError(f"{self} failed to capture frames during warmup.") except Exception as e: self._cleanup() @@ -131,15 +166,14 @@ class ZMQCamera(Camera): @staticmethod def find_cameras() -> list[dict[str, Any]]: - """ZMQ cameras require manual configuration (server address/port).""" - return [] - - def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]: """ - Read a single frame from the ZMQ camera. + Detection not implemented for ZMQ cameras. These cameras require manual configuration (server address/port). + """ + raise NotImplementedError("Camera detection is not implemented for ZMQ cameras.") - Returns: - np.ndarray: Decoded frame (height, width, 3) + def _read_from_hardware(self) -> NDArray[Any]: + """ + Reads a single frame directly from the ZMQ socket. """ if not self.is_connected or self.socket is None: raise DeviceNotConnectedError(f"{self} is not connected.") @@ -147,6 +181,7 @@ class ZMQCamera(Camera): try: message = self.socket.recv_string() except Exception as e: + # Check for ZMQ timeout (EAGAIN/Again) without requiring global zmq import if type(e).__name__ == "Again": raise TimeoutError(f"{self} timeout after {self.timeout_ms}ms") from e raise @@ -176,42 +211,117 @@ class ZMQCamera(Camera): return frame - def _read_loop(self) -> None: - while self.stop_event and not self.stop_event.is_set(): - try: - frame = self.read() - with self.frame_lock: - self.latest_frame = frame - self.new_frame_event.set() - except DeviceNotConnectedError: - break - except TimeoutError: - pass - except Exception as e: - logger.warning(f"Read error: {e}") + def read(self, color_mode: ColorMode | None = None) -> NDArray[Any]: + """ + Reads a single frame synchronously from the camera. - def _start_read_thread(self) -> None: - if self.thread and self.thread.is_alive(): - return - self.stop_event = Event() - self.thread = Thread(target=self._read_loop, daemon=True) - self.thread.start() + This is a blocking call. It waits for the next available frame from the + camera background thread. - def _stop_read_thread(self) -> None: - if self.stop_event: - self.stop_event.set() - if self.thread and self.thread.is_alive(): - self.thread.join(timeout=2.0) - self.thread = None - self.stop_event = None + Returns: + np.ndarray: Decoded frame (height, width, 3) + """ + start_time = time.perf_counter() + + if color_mode is not None: + logger.warning( + f"{self} read() color_mode parameter is deprecated and will be removed in future versions." + ) - def async_read(self, timeout_ms: float = 10000) -> NDArray[Any]: - """Read latest frame asynchronously (non-blocking).""" if not self.is_connected: raise DeviceNotConnectedError(f"{self} is not connected.") - if not self.thread or not self.thread.is_alive(): - self._start_read_thread() + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") + + self.new_frame_event.clear() + frame = self.async_read(timeout_ms=10000) + + read_duration_ms = (time.perf_counter() - start_time) * 1e3 + logger.debug(f"{self} read took: {read_duration_ms:.1f}ms") + + return frame + + def _read_loop(self) -> None: + """ + Internal loop run by the background thread for asynchronous reading. + """ + if self.stop_event is None: + raise RuntimeError(f"{self}: stop_event is not initialized.") + + failure_count = 0 + while not self.stop_event.is_set(): + try: + frame = self._read_from_hardware() + capture_time = time.perf_counter() + + with self.frame_lock: + self.latest_frame = frame + self.latest_timestamp = capture_time + self.new_frame_event.set() + failure_count = 0 + + except DeviceNotConnectedError: + break + except (TimeoutError, Exception) as e: + if failure_count <= 10: + failure_count += 1 + logger.warning(f"Read error: {e}") + else: + raise RuntimeError(f"{self} exceeded maximum consecutive read failures.") from e + + def _start_read_thread(self) -> None: + if self.stop_event is not None: + self.stop_event.set() + if self.thread is not None and self.thread.is_alive(): + self.thread.join(timeout=2.0) + + with self.frame_lock: + self.latest_frame = None + self.latest_timestamp = None + self.new_frame_event.clear() + + self.stop_event = Event() + self.thread = Thread(target=self._read_loop, daemon=True, name=f"{self}_read_loop") + self.thread.start() + time.sleep(0.1) + + def _stop_read_thread(self) -> None: + if self.stop_event is not None: + self.stop_event.set() + + if self.thread is not None and self.thread.is_alive(): + self.thread.join(timeout=2.0) + + self.thread = None + self.stop_event = None + + with self.frame_lock: + self.latest_frame = None + self.latest_timestamp = None + self.new_frame_event.clear() + + def async_read(self, timeout_ms: float = 200) -> NDArray[Any]: + """ + Reads the latest available frame asynchronously. + + Args: + timeout_ms (float): Maximum time in milliseconds to wait for a frame + to become available. Defaults to 200ms. + + Returns: + np.ndarray: The latest captured frame. + + Raises: + DeviceNotConnectedError: If the camera is not connected. + TimeoutError: If no frame data becomes available within the specified timeout. + RuntimeError: If the background thread is not running. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0): raise TimeoutError(f"{self} async_read timeout after {timeout_ms}ms") @@ -225,11 +335,55 @@ class ZMQCamera(Camera): return frame + def read_latest(self, max_age_ms: int = 1000) -> NDArray[Any]: + """Return the most recent frame captured immediately (Peeking). + + This method is non-blocking and returns whatever is currently in the + memory buffer. The frame may be stale, + meaning it could have been captured a while ago (hanging camera scenario e.g.). + + Returns: + NDArray[Any]: The frame image (numpy array). + + Raises: + TimeoutError: If the latest frame is older than `max_age_ms`. + DeviceNotConnectedError: If the camera is not connected. + RuntimeError: If the camera is connected but has not captured any frames yet. + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + if self.thread is None or not self.thread.is_alive(): + raise RuntimeError(f"{self} read thread is not running.") + + with self.frame_lock: + frame = self.latest_frame + timestamp = self.latest_timestamp + + if frame is None or timestamp is None: + raise RuntimeError(f"{self} has not captured any frames yet.") + + age_ms = (time.perf_counter() - timestamp) * 1e3 + if age_ms > max_age_ms: + raise TimeoutError( + f"{self} latest frame is too old: {age_ms:.1f} ms (max allowed: {max_age_ms} ms)." + ) + + return frame + def disconnect(self) -> None: """Disconnect from ZMQ camera.""" - if not self.is_connected and not self.thread: + if not self.is_connected and self.thread is None: raise DeviceNotConnectedError(f"{self} not connected.") - self._stop_read_thread() + if self.thread is not None: + self._stop_read_thread() + self._cleanup() + + with self.frame_lock: + self.latest_frame = None + self.latest_timestamp = None + self.new_frame_event.clear() + logger.info(f"{self} disconnected.") diff --git a/src/lerobot/cameras/zmq/configuration_zmq.py b/src/lerobot/cameras/zmq/configuration_zmq.py index 027ae12b5..4e7732cfc 100644 --- a/src/lerobot/cameras/zmq/configuration_zmq.py +++ b/src/lerobot/cameras/zmq/configuration_zmq.py @@ -29,6 +29,7 @@ class ZMQCameraConfig(CameraConfig): camera_name: str = "zmq_camera" color_mode: ColorMode = ColorMode.RGB timeout_ms: int = 5000 + warmup_s: int = 1 def __post_init__(self) -> None: if self.color_mode not in (ColorMode.RGB, ColorMode.BGR): diff --git a/src/lerobot/configs/policies.py b/src/lerobot/configs/policies.py index 7f326b70b..44b013c29 100644 --- a/src/lerobot/configs/policies.py +++ b/src/lerobot/configs/policies.py @@ -45,12 +45,12 @@ class PreTrainedConfig(draccus.ChoiceRegistry, HubMixin, abc.ABC): # type: igno Args: n_obs_steps: Number of environment steps worth of observations to pass to the policy (takes the current step and additional steps going back). - input_shapes: A dictionary defining the shapes of the input data for the policy. - output_shapes: A dictionary defining the shapes of the output data for the policy. - input_normalization_modes: A dictionary with key representing the modality and the value specifies the - normalization mode to apply. - output_normalization_modes: Similar dictionary as `input_normalization_modes`, but to unnormalize to - the original scale. + input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents + the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents + the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to + a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX) """ n_obs_steps: int = 1 diff --git a/src/lerobot/data_processing/annotations/annotate_libero.sh b/src/lerobot/data_processing/annotations/annotate_libero.sh new file mode 100644 index 000000000..c096e13e7 --- /dev/null +++ b/src/lerobot/data_processing/annotations/annotate_libero.sh @@ -0,0 +1,50 @@ +#!/bin/bash + +# Example script to run synthetic data generation with Qwen VLM +# This generates user prompts and robot utterances for hierarchical policy training + +# Configuration +REPO_ID="lerobot/libero_10" +MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct" +# or: MODEL="Qwen/Qwen2-VL-7B-Instruct" + + +OUTPUT_DIR="/fsx/jade_choghari/outputs/libero-10-annotate-high" + +BATCH_SIZE=16 +TEMPERATURE=0.9 +SAMPLE_INTERVAL=5.0 # generate dialogue every 1 second (all episodes processed) + +# Run subtask annotation +# python /admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \ +# --repo-id "$REPO_ID" \ +# --video-key observation.images.image \ +# --output-dir "$OUTPUT_DIR" \ +# --skip-existing \ +# --output-repo-id "jadechoghari/libero10-annotate" \ +# --batch-size "$BATCH_SIZE" \ +# run synthetic data generation (all episodes processed) +# python examples/dataset/annotate_pgen.py \ +# --repo-id "$REPO_ID" \ +# --model "$MODEL" \ +# --output-dir "$OUTPUT_DIR" \ +# --temperature "$TEMPERATURE" \ +# --batch-size "$BATCH_SIZE" \ +# --sample-interval "$SAMPLE_INTERVAL" \ +# --image-key observation.images.base \ +# --num-image-views-per-sample 1 + +# for faster testing, increase sample interval: +# --sample-interval 5.0 # Samples every 5 seconds (much faster) + +# to push to hub after generation: +# add --push-to-hub flag + +# efficient batch processing: 4 episodes at once +python src/lerobot/data_processing/annotations/high_level_annotate.py \ + --data-dir "/fsx/jade_choghari/outputs/libero-10-annotate" \ + --output-dir "$OUTPUT_DIR" \ + --video-mode \ + --video-key observation.images.image \ + --video-batch-size "$BATCH_SIZE" \ + --sample-interval 5.0 diff --git a/src/lerobot/data_processing/annotations/high_level_annotate.py b/src/lerobot/data_processing/annotations/high_level_annotate.py new file mode 100644 index 000000000..a37d4b6fa --- /dev/null +++ b/src/lerobot/data_processing/annotations/high_level_annotate.py @@ -0,0 +1,1531 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Synthetic Data Generation for Hierarchical Policy Training. + +This script generates synthetic user prompts (ℓ_t) and robot utterances (u_t) for +hierarchical policy training using Qwen VLM as the generator model (pgen). + +The pipeline: +1. Loads a LeRobot dataset with skill annotations (from annotate.py) +2. For each frame, generates synthetic dialogue based on: + - Visual context (images at time t OR video clips in video mode) + - Current skill being performed + - History of previous skills + - High-level task description +3. Saves results as high-level tasks and updates dataset with task_index_high_level + +Modes: +- Image Mode (default): Samples frames at intervals and sends images to the model +- Video Mode (--video-mode): Passes entire skill video clips to the model + +Usage: +```bash +# Image mode (default) +python examples/dataset/annotate_pgen.py \ + --repo-id lerobot/svla_so101_pickplace \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --output-dir /path/to/output + +# Video mode with batch processing +python examples/dataset/annotate_pgen.py \ + --repo-id lerobot/svla_so101_pickplace \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --video-mode \ + --video-key observation.images.base \ + --video-batch-size 4 +``` +""" + +import argparse +import json +import re +import subprocess +import tempfile +import textwrap +from pathlib import Path +from typing import Any + +import cv2 +import numpy as np +import pandas as pd +import torch +from PIL import Image +from rich.console import Console +from rich.progress import Progress, SpinnerColumn, TextColumn +from tqdm import tqdm + +from lerobot.datasets.dataset_tools import add_features +from lerobot.datasets.lerobot_dataset import LeRobotDataset + + +# Video Extraction Utilities +class VideoExtractor: + """Utilities for extracting and processing video segments from LeRobot datasets.""" + + def __init__(self, console: Console | None = None): + self.console = console or Console() + + def extract_episode_video( + self, + video_path: Path, + start_timestamp: float, + end_timestamp: float, + target_fps: int = 1, + ) -> Path: + """ + Extract a specific episode segment from a concatenated video file. + + Args: + video_path: Path to the source video file + start_timestamp: Start time in seconds + end_timestamp: End time in seconds + target_fps: Target frames per second for output + + Returns: + Path to the extracted temporary video file + """ + tmp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) + tmp_path = Path(tmp_file.name) + tmp_file.close() + + duration = end_timestamp - start_timestamp + + cmd = [ + "ffmpeg", + "-i", + str(video_path), + "-ss", + str(start_timestamp), + "-t", + str(duration), + "-r", + str(target_fps), + "-c:v", + "libx264", + "-preset", + "ultrafast", + "-crf", + "23", + "-an", + "-y", + str(tmp_path), + ] + + try: + subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True) + except subprocess.CalledProcessError as e: + raise RuntimeError(f"FFmpeg failed: {e}") from e + except FileNotFoundError: + raise RuntimeError("FFmpeg not found. Please install ffmpeg.") + + if not tmp_path.exists() or tmp_path.stat().st_size < 1024: + if tmp_path.exists(): + tmp_path.unlink() + raise RuntimeError("Video extraction produced invalid file") + + return tmp_path + + def get_video_duration(self, video_path: Path) -> float: + """Get duration of a video file in seconds.""" + cap = cv2.VideoCapture(str(video_path)) + fps = cap.get(cv2.CAP_PROP_FPS) or 30 + frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + cap.release() + return frame_count / fps + + +# Prompt Template for pgen + +PGEN_PROMPT_TEMPLATE_IMAGE = textwrap.dedent("""\ + # Role + You are a robot-assistant dialogue generator for hierarchical robot policies. + + # Task + You will receive: + - A list of images showing the current robot scene at time t + - The high-level task: {task_description} + - Previous skill steps completed: {skill_history} + - The next skill to be performed by the robot: {skill_current} + + # Your Goal + Generate two things that create a natural human-robot interaction: + 1. **user_prompt**: A natural-sounding user request that logically leads to the robot + performing the skill "{skill_current}" given the task context and history. + 2. **robot_utterance**: A natural robot reply acknowledging or clarifying the request. + + # Guidelines + - The user prompt should be grounded in the visual scene and task context + - Vary interaction types: direct commands, implicit requests, corrections, constraints + - Examples of user prompt styles: + * Direct: "Can you pick up the red brick?" + * Implicit: "I need something red for the tower" + * Negative: "Don't pick up the blue one" + * Constraint: "Make sure to handle it gently" + * Correction: "Actually, move to the other box instead" + - Robot responses should be appropriate: confirmations, clarifications, or error handling + - Use the skill history to ensure continuity (don't repeat past actions) + - Consider world knowledge (dietary preferences, object properties, etc.) + + # Scenario Types (choose one that fits): + - **specific_object**: User specifies exact object/action + - **negative_task**: User says what NOT to do + - **situated_correction**: User adjusts based on current state + - **implicit_request**: User implies need without direct command + - **constraint_based**: User adds specific constraints + + # Response Types (choose one that fits): + - **confirmation**: Simple "OK, I'll do X" + - **clarification**: "Just to confirm, you want me to..." + - **acknowledgment**: "Got it, [doing action]" + - **constraint_acknowledgment**: "Sure, I'll [action] while [constraint]" + + # Output Format + Respond ONLY with valid JSON: + {{ + "scenario_type": "one of the types above", + "response_type": "one of the types above", + "user_prompt": "natural user request here", + "robot_utterance": "natural robot response here" + }} + + The responses must be grounded in the visual scene, the task, and the skill history. + Make it sound like a real human-robot interaction. + """) + +PGEN_PROMPT_TEMPLATE_VIDEO = textwrap.dedent("""\ + # Role + You are a robot-assistant dialogue generator for hierarchical robot policies. + + # Task + You are watching a full robot demonstration video for the task: {task_description} + + For each timestamp below, generate natural human-robot dialogue that would have led to the observed behavior. + At each timestamp, you'll see: + - What skills have been completed so far (cumulative history) + - What skill is currently being executed + + {timestamp_context} + + # Your Goal + For EACH timestamp, generate: + 1. **user_prompt**: A natural user request that would lead to the robot performing the current skill + 2. **robot_utterance**: A natural robot response acknowledging the request + + # Guidelines + - Watch the video from start to each timestamp to understand the context + - Ground prompts in what's visible in the video at that time + - Vary interaction types: direct commands, implicit requests, corrections, constraints + - Examples of user prompt styles: + * Direct: "Can you pick up the red brick?" + * Implicit: "I need something red for the tower" + * Negative: "Don't pick up the blue one" + * Constraint: "Make sure to handle it gently" + * Correction: "Actually, move to the other box instead" + - Robot responses should be appropriate: confirmations, clarifications, or error handling + - Ensure continuity across timestamps (don't contradict earlier dialogue) + - Consider world knowledge (dietary preferences, object properties, etc.) + + # Scenario Types: + - **specific_object**: User specifies exact object/action + - **negative_task**: User says what NOT to do + - **situated_correction**: User adjusts based on current state + - **implicit_request**: User implies need without direct command + - **constraint_based**: User adds specific constraints + + # Response Types: + - **confirmation**: Simple "OK, I'll do X" + - **clarification**: "Just to confirm, you want me to..." + - **acknowledgment**: "Got it, [doing action]" + - **constraint_acknowledgment**: "Sure, I'll [action] while [constraint]" + + # Output Format + Respond ONLY with valid JSON array: + [ + {{ + "timestamp": timestamp_value, + "scenario_type": "one of the types above", + "response_type": "one of the types above", + "user_prompt": "natural user request here", + "robot_utterance": "natural robot response here" + }}, + ... (one entry per timestamp) + ] + + Make it sound like a real human-robot interaction grounded in the video. + """) + + +def construct_prompt_image( + task_description: str, + skill_history: list[str], + skill_current: str, +) -> str: + """ + Construct the text prompt for pgen in image mode. + + Args: + task_description: High-level task description + skill_history: List of previously completed skills + skill_current: Current skill to be performed + + Returns: + Formatted prompt string + """ + # Format skill history nicely + if skill_history: + history_str = ", ".join(f'"{s}"' for s in skill_history[-5:]) # Last 5 for context + if len(skill_history) > 5: + history_str = f"... {history_str}" + else: + history_str = "None (starting the task)" + + return PGEN_PROMPT_TEMPLATE_IMAGE.format( + task_description=task_description, + skill_history=history_str, + skill_current=skill_current, + ) + + +def construct_prompt_video( + task_description: str, + timestamps_with_skills: list[dict], +) -> str: + """ + Construct the text prompt for pgen in video mode. + + Args: + task_description: High-level task description + timestamps_with_skills: List of dicts with keys: + - timestamp: float + - skills_so_far: list[str] + - current_skill: str + + Returns: + Formatted prompt string + """ + # Build timestamp context + timestamp_lines = [] + for item in timestamps_with_skills: + ts = item["timestamp"] + skills_so_far = item["skills_so_far"] + current_skill = item["current_skill"] + + if skills_so_far: + skills_str = ", ".join(f'"{s}"' for s in skills_so_far) + else: + skills_str = "None (starting)" + + timestamp_lines.append( + f"- **Timestamp {ts:.2f}s**: Skills completed: [{skills_str}] | Current skill: \"{current_skill}\"" + ) + + timestamp_context = "\n".join(timestamp_lines) + + return PGEN_PROMPT_TEMPLATE_VIDEO.format( + task_description=task_description, + timestamp_context=timestamp_context, + ) + + +# Qwen VLM Interface + +class QwenPgen: + """Qwen VLM wrapper for synthetic dialogue generation.""" + + def __init__( + self, + model_name: str, + device: str = "cuda", + torch_dtype: torch.dtype = torch.bfloat16, + temperature: float = 0.7, + ): + from qwen_vl_utils import process_vision_info + from transformers import AutoProcessor, Qwen2VLForConditionalGeneration + + self.console = Console() + self.device = device + self.model_name = model_name + self.temperature = temperature + self.process_vision_info = process_vision_info + + self.console.print(f"[cyan]Loading Qwen model: {model_name}...[/cyan]") + + # Load model based on name + if "qwen3" in model_name.lower(): + from transformers import Qwen3VLMoeForConditionalGeneration + self.model = Qwen3VLMoeForConditionalGeneration.from_pretrained( + model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True + ) + else: + self.model = Qwen2VLForConditionalGeneration.from_pretrained( + model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True + ) + + self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) + self.console.print(f"[green]✓ Model loaded successfully on {device}[/green]") + + def call_qwen( + self, + images: list[Image.Image | str] | None = None, + prompt: str = "", + video: str | Path | None = None, + ) -> dict[str, str]: + """ + Call Qwen VLM to generate synthetic dialogue for a single request. + + Args: + images: List of PIL Images or image paths (for image mode) + prompt: Text prompt for generation + video: Path to video file (for video mode) + + Returns: + Dictionary with keys: scenario_type, response_type, user_prompt, robot_utterance + """ + # Use batch method with single item + results = self.call_qwen_batch( + batch_images=[images] if images else [None], + batch_prompts=[prompt], + batch_videos=[video] if video else [None], + ) + return results[0] + + def call_qwen_batch( + self, + batch_images: list[list[Image.Image | str] | None], + batch_prompts: list[str], + batch_videos: list[str | Path | None] | None = None, + ) -> list[dict[str, str]]: + """ + Call Qwen VLM to generate synthetic dialogue for a batch of requests. + + Args: + batch_images: List of image lists, one per request (None for video mode) + batch_prompts: List of text prompts, one per request + batch_videos: List of video paths, one per request (None for image mode) + + Returns: + List of dictionaries, each with keys: scenario_type, response_type, user_prompt, robot_utterance + """ + if batch_videos is None: + batch_videos = [None] * len(batch_images) + + if len(batch_images) != len(batch_prompts) or len(batch_images) != len(batch_videos): + raise ValueError( + f"Batch size mismatch: {len(batch_images)} image lists vs " + f"{len(batch_prompts)} prompts vs {len(batch_videos)} videos" + ) + + batch_size = len(batch_images) + if batch_size == 0: + return [] + + # Build messages for each item in batch + all_messages = [] + for images, prompt, video in zip(batch_images, batch_prompts, batch_videos): + content = [] + + # Add video or images + if video is not None: + # Video mode + content.append({"type": "video", "video": str(video), "fps": 1.0}) + elif images is not None: + # Image mode + for img in images: + if isinstance(img, str): + content.append({"type": "image", "image": img}) + else: + # PIL Image + content.append({"type": "image", "image": img}) + + content.append({"type": "text", "text": prompt}) + + messages = [ + { + "role": "user", + "content": content, + } + ] + all_messages.append(messages) + + # Process all inputs + texts = [] + all_image_inputs = [] + all_video_inputs = [] + + for messages in all_messages: + text = self.processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + texts.append(text) + + image_inputs, video_inputs = self.process_vision_info(messages) + all_image_inputs.append(image_inputs) + all_video_inputs.append(video_inputs) + + # Flatten image and video inputs for batch processing + # The processor expects a flat list of images across all batch items + flat_images = [] + for img_list in all_image_inputs: + if img_list is not None: + if isinstance(img_list, list): + flat_images.extend(img_list) + else: + flat_images.append(img_list) + + flat_videos = [] + for vid_list in all_video_inputs: + if vid_list is not None: + if isinstance(vid_list, list): + flat_videos.extend(vid_list) + else: + flat_videos.append(vid_list) + + # Process batch + inputs = self.processor( + text=texts, + images=flat_images if flat_images else None, + videos=flat_videos if flat_videos else None, + padding=True, + return_tensors="pt", + ).to(self.device) + + # Generate + with torch.no_grad(): + generated_ids = self.model.generate( + **inputs, + max_new_tokens=512, + do_sample=True, + temperature=self.temperature, + ) + + # Decode responses + responses = self.processor.batch_decode( + [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)], + skip_special_tokens=True, + ) + + # Parse all responses + results = [] + for response in responses: + try: + parsed = self._parse_response(response.strip()) + results.append(parsed) + except Exception as e: + self.console.print(f"[yellow]Warning: Failed to parse response: {e}[/yellow]") + # Return empty/default result + results.append({ + "scenario_type": "specific_object", + "response_type": "confirmation", + "user_prompt": "", + "robot_utterance": "", + }) + + return results + + def _parse_response(self, response: str) -> dict[str, str]: + """Parse JSON response from model.""" + # Extract JSON from response + if "```json" in response: + response = response.split("```json")[1].split("```")[0] + elif "```" in response: + response = response.split("```")[1].split("```")[0] + + try: + data = json.loads(response) + return { + "scenario_type": data.get("scenario_type", "specific_object"), + "response_type": data.get("response_type", "confirmation"), + "user_prompt": data.get("user_prompt", ""), + "robot_utterance": data.get("robot_utterance", ""), + } + except json.JSONDecodeError: + # Try to find JSON object in response + match = re.search(r"\{.*\}", response, re.DOTALL) + if match: + data = json.loads(match.group()) + return { + "scenario_type": data.get("scenario_type", "specific_object"), + "response_type": data.get("response_type", "confirmation"), + "user_prompt": data.get("user_prompt", ""), + "robot_utterance": data.get("robot_utterance", ""), + } + + raise ValueError(f"Could not parse response: {response[:200]}...") + + +# Annotation Pipeline + +def load_skills_metadata(dataset_root: Path) -> dict | None: + """Load skills.json metadata from annotated dataset.""" + skills_path = dataset_root / "meta" / "skills.json" + if skills_path.exists(): + with open(skills_path) as f: + return json.load(f) + return None + + +def get_skill_at_timestamp(skills: list[dict], timestamp: float) -> str | None: + """Find which skill covers a given timestamp.""" + for skill in skills: + if skill["start"] <= timestamp < skill["end"]: + return skill["name"] + # Handle last frame + if timestamp >= skill["end"] and skill == skills[-1]: + return skill["name"] + return skills[-1]["name"] if skills else None + + +def annotate_sample_image( + pgen: QwenPgen, + images: list[Image.Image | str], + task_description: str, + skill_history: list[str], + skill_current: str, +) -> dict[str, str]: + """ + Generate synthetic dialogue for a single sample using images. + + Args: + pgen: Qwen model wrapper + images: List of images at current timestep + task_description: High-level task description + skill_history: Previous skills completed + skill_current: Current skill being performed + + Returns: + Dictionary with generated dialogue + """ + prompt = construct_prompt_image(task_description, skill_history, skill_current) + result = pgen.call_qwen(images=images, prompt=prompt, video=None) + return result + + +def annotate_episode_video( + pgen: QwenPgen, + video: str | Path, + task_description: str, + timestamps_with_skills: list[dict], +) -> list[dict[str, Any]]: + """ + Generate synthetic dialogue for an entire episode using video. + + Args: + pgen: Qwen model wrapper + video: Path to episode video file + task_description: High-level task description + timestamps_with_skills: List of dicts with timestamp, skills_so_far, current_skill + + Returns: + List of dictionaries with generated dialogue, one per timestamp + """ + # Use batch method with single episode + results = annotate_episodes_video_batch( + pgen=pgen, + batch_videos=[video], + batch_task_descriptions=[task_description], + batch_timestamps_with_skills=[timestamps_with_skills], + ) + return results[0] + + +def annotate_episodes_video_batch( + pgen: QwenPgen, + batch_videos: list[str | Path], + batch_task_descriptions: list[str], + batch_timestamps_with_skills: list[list[dict]], +) -> list[list[dict[str, Any]]]: + """ + Generate synthetic dialogue for multiple episodes using videos in batch. + + Args: + pgen: Qwen model wrapper + batch_videos: List of paths to episode video files + batch_task_descriptions: List of high-level task descriptions + batch_timestamps_with_skills: List of timestamp lists, one per episode + + Returns: + List of result lists, one per episode (each containing dicts with generated dialogue) + """ + batch_size = len(batch_videos) + if batch_size == 0: + return [] + + # Build messages for each episode + all_messages = [] + for video, task_desc, timestamps_with_skills in zip( + batch_videos, batch_task_descriptions, batch_timestamps_with_skills + ): + prompt = construct_prompt_video(task_desc, timestamps_with_skills) + + content = [ + {"type": "video", "video": str(video), "fps": 1.0}, + {"type": "text", "text": prompt}, + ] + + messages = [{"role": "user", "content": content}] + all_messages.append(messages) + + # Process all episodes through Qwen in batch + all_texts = [] + all_image_inputs = [] + all_video_inputs = [] + + for messages in all_messages: + text = pgen.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + image_inputs, video_inputs = pgen.process_vision_info(messages) + all_texts.append(text) + all_image_inputs.extend(image_inputs or []) + all_video_inputs.extend(video_inputs or []) + + inputs = pgen.processor( + text=all_texts, + images=all_image_inputs if all_image_inputs else None, + videos=all_video_inputs if all_video_inputs else None, + padding=True, + return_tensors="pt", + ).to(pgen.device) + + with torch.no_grad(): + generated_ids = pgen.model.generate( + **inputs, + max_new_tokens=2048, # Larger for multiple timestamps per episode + do_sample=True, + temperature=pgen.temperature, + ) + + responses = pgen.processor.batch_decode( + [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)], + skip_special_tokens=True, + ) + + # Parse each response + all_results = [] + for response, timestamps_with_skills in zip(responses, batch_timestamps_with_skills): + results = _parse_video_response(response.strip(), timestamps_with_skills) + all_results.append(results) + + return all_results + + +def _parse_video_response(response: str, timestamps_with_skills: list[dict]) -> list[dict[str, Any]]: + """Parse JSON array response from video mode.""" + # Extract JSON from response + if "```json" in response: + response = response.split("```json")[1].split("```")[0] + elif "```" in response: + response = response.split("```")[1].split("```")[0] + + try: + data = json.loads(response) + if not isinstance(data, list): + # If it's a dict with a list inside + if "annotations" in data: + data = data["annotations"] + elif "results" in data: + data = data["results"] + else: + raise ValueError("Expected JSON array or dict with 'annotations'/'results' key") + + results = [] + for item in data: + results.append({ + "timestamp": item.get("timestamp", 0.0), + "scenario_type": item.get("scenario_type", "specific_object"), + "response_type": item.get("response_type", "confirmation"), + "user_prompt": item.get("user_prompt", ""), + "robot_utterance": item.get("robot_utterance", ""), + }) + + return results + + except json.JSONDecodeError: + # Try to find JSON array in response + match = re.search(r"\[.*\]", response, re.DOTALL) + if match: + data = json.loads(match.group()) + results = [] + for item in data: + results.append({ + "timestamp": item.get("timestamp", 0.0), + "scenario_type": item.get("scenario_type", "specific_object"), + "response_type": item.get("response_type", "confirmation"), + "user_prompt": item.get("user_prompt", ""), + "robot_utterance": item.get("robot_utterance", ""), + }) + return results + + # Fallback: return empty results for each timestamp + print(f"Warning: Could not parse video response: {response[:200]}...") + return [ + { + "timestamp": ts["timestamp"], + "scenario_type": "specific_object", + "response_type": "confirmation", + "user_prompt": "", + "robot_utterance": "", + } + for ts in timestamps_with_skills + ] + + + + +def _generate_synthetic_data_video_mode( + dataset: LeRobotDataset, + pgen: QwenPgen, + skills_metadata: dict, + video_key: str, + video_extractor: VideoExtractor, + console: Console, + sample_interval_seconds: float = 1.0, + batch_size: int = 1, +) -> tuple[pd.DataFrame, np.ndarray, list[dict]]: + """ + Generate synthetic dialogue data using video mode with batched VLM calls. + + The VLM sees full episode videos and generates dialogue for multiple + timestamps per episode, with cumulative skill history at each timestamp. + + Args: + dataset: LeRobot dataset with skill annotations + pgen: Qwen model wrapper + skills_metadata: Loaded skills.json metadata + video_key: Video observation key (e.g., 'observation.images.base') + video_extractor: VideoExtractor instance + console: Rich console for logging + sample_interval_seconds: Sample timestamps at this interval + batch_size: Number of episodes to process in each VLM batch call + + Returns: + Tuple of (tasks_df, task_indices_array, debug_outputs) + """ + coarse_description = skills_metadata.get("coarse_description", "Complete the task") + episodes = skills_metadata.get("episodes", {}) + + # Track unique high-level tasks + high_level_tasks = {} + task_index_counter = 0 + + # Array to store task index for each frame + full_dataset_length = len(dataset) + task_indices = np.zeros(full_dataset_length, dtype=np.int64) + + debug_outputs = [] + timestamps_processed = 0 + + console.print(f"[cyan]Processing {len(episodes)} episodes in VIDEO MODE with batch_size={batch_size}...[/cyan]") + console.print(f"[cyan]Sampling interval: {sample_interval_seconds}s[/cyan]") + + # Convert episodes dict to list for batching + episode_list = list(episodes.items()) + + # Process episodes in batches + for batch_start in tqdm(range(0, len(episode_list), batch_size), desc="Processing episode batches"): + batch_end = min(batch_start + batch_size, len(episode_list)) + batch_episodes = episode_list[batch_start:batch_end] + + # Collect data for this batch + batch_data = [] + extracted_videos = [] + + for episode_key, episode_data in batch_episodes: + episode_idx = int(episode_key) + skills = episode_data.get("skills", []) + description = episode_data.get("description", coarse_description) + + if not skills: + console.print(f"[yellow]Warning: Episode {episode_idx} has no skills[/yellow]") + continue + + # Get video path and extract full episode + extracted_path = None + try: + video_path = dataset.root / dataset.meta.get_video_file_path(episode_idx, video_key) + if not video_path.exists(): + console.print(f"[yellow]Warning: Video not found for episode {episode_idx}[/yellow]") + continue + + # Get episode timestamps + ep = dataset.meta.episodes[episode_idx] + episode_start_ts = float(ep[f"videos/{video_key}/from_timestamp"]) + episode_end_ts = float(ep[f"videos/{video_key}/to_timestamp"]) + duration = episode_end_ts - episode_start_ts + + # Extract FULL episode video + extracted_path = video_extractor.extract_episode_video( + video_path, episode_start_ts, episode_end_ts, target_fps=1 + ) + extracted_videos.append(extracted_path) + + except Exception as e: + console.print(f"[yellow]Warning: Failed to extract video for episode {episode_idx}: {e}[/yellow]") + continue + + # Build list of timestamps to sample + timestamps_with_skills = [] + current_time = 0.0 + + while current_time <= duration: + # Find which skill is active at this timestamp + current_skill = None + skills_so_far = [] + + for skill in skills: + if skill["end"] <= current_time: + skills_so_far.append(skill["name"]) + elif skill["start"] <= current_time < skill["end"]: + current_skill = skill["name"] + break + elif current_time >= skill["end"] and skill == skills[-1]: + current_skill = skill["name"] + break + + if current_skill: + timestamps_with_skills.append({ + "timestamp": current_time, + "skills_so_far": skills_so_far.copy(), + "current_skill": current_skill, + }) + + current_time += sample_interval_seconds + + if not timestamps_with_skills: + console.print(f"[yellow]Warning: No valid timestamps for episode {episode_idx}[/yellow]") + continue + + # Store batch item + batch_data.append({ + "episode_idx": episode_idx, + "episode_metadata": ep, + "video_path": extracted_path, + "task_description": description, + "timestamps_with_skills": timestamps_with_skills, + "skills": skills, + }) + + if not batch_data: + continue + + # BATCHED VLM CALL for all episodes in batch + try: + batch_results = annotate_episodes_video_batch( + pgen=pgen, + batch_videos=[item["video_path"] for item in batch_data], + batch_task_descriptions=[item["task_description"] for item in batch_data], + batch_timestamps_with_skills=[item["timestamps_with_skills"] for item in batch_data], + ) + + # Process results for each episode in batch + for item, results in zip(batch_data, batch_results): + episode_idx = item["episode_idx"] + ep = item["episode_metadata"] + timestamps_with_skills = item["timestamps_with_skills"] + description = item["task_description"] + + timestamps_processed += len(results) + + # Map results back to timestamps and create task indices + timestamp_to_result = {} + for result in results: + ts = result["timestamp"] + timestamp_to_result[ts] = result + + # Process each sampled timestamp + for ts_info in timestamps_with_skills: + ts = ts_info["timestamp"] + result = timestamp_to_result.get(ts, { + "timestamp": ts, + "scenario_type": "specific_object", + "response_type": "confirmation", + "user_prompt": "", + "robot_utterance": "", + }) + + # Create unique task key + task_key = ( + result["user_prompt"], + result["robot_utterance"], + ts_info["current_skill"], + result["scenario_type"], + result["response_type"], + ) + + # Assign or create task index + if task_key not in high_level_tasks: + high_level_tasks[task_key] = task_index_counter + task_index_counter += 1 + + current_task_idx = high_level_tasks[task_key] + + # Find all frames at this timestamp and assign task_idx + ep_from = ep["dataset_from_index"] + ep_to = ep["dataset_to_index"] + + for frame_idx in range(ep_from, ep_to): + frame = dataset[frame_idx] + frame_ts = frame["timestamp"].item() + + # Assign to closest sampled timestamp + if abs(frame_ts - ts) < sample_interval_seconds / 2: + task_indices[frame_idx] = current_task_idx + + # Save for debugging + debug_outputs.append({ + "episode_id": int(episode_idx), + "timestamp": float(ts), + "skill_current": ts_info["current_skill"], + "skills_so_far": ts_info["skills_so_far"], + "task_description": description, + "video_mode": True, + **result, + }) + + finally: + # Clean up extracted videos + for extracted_path in extracted_videos: + if extracted_path and extracted_path.exists(): + extracted_path.unlink() + + console.print(f"[green]✓ Processed {timestamps_processed} timestamps across {len(episodes)} episodes[/green]") + + # Create tasks DataFrame + tasks_data = [] + for task_key, task_idx in sorted(high_level_tasks.items(), key=lambda x: x[1]): + user_prompt, robot_utterance, skill, scenario_type, response_type = task_key + tasks_data.append({ + "task": f"{user_prompt} | {robot_utterance}", + "task_index": task_idx, + "user_prompt": user_prompt, + "robot_utterance": robot_utterance, + "skill": skill, + "scenario_type": scenario_type, + "response_type": response_type, + }) + + tasks_df = pd.DataFrame(tasks_data).set_index("task") + console.print(f"[green]✓ Generated {len(high_level_tasks)} unique high-level tasks[/green]") + + return tasks_df, task_indices, debug_outputs + + +def generate_synthetic_data( + dataset: LeRobotDataset, + pgen: QwenPgen, + skills_metadata: dict, + image_keys: list[str], + sample_interval_seconds: float = 1.0, + console: Console | None = None, + video_mode: bool = False, + video_key: str | None = None, + video_batch_size: int = 1, +) -> tuple[pd.DataFrame, np.ndarray, list[dict]]: + """ + Generate synthetic dialogue data for entire dataset. + + This function processes ALL frames in the dataset, but only calls the VLM + at specified intervals (sample_interval_seconds). Frames between samples + inherit the task_index from the most recent sample. + + Args: + dataset: LeRobot dataset with skill annotations + pgen: Qwen model wrapper + skills_metadata: Loaded skills.json metadata + image_keys: List of image observation keys to use (for image mode) + sample_interval_seconds: Generate dialogue every N seconds (default: 1.0) + console: Rich console for logging + video_mode: If True, use video clips instead of sampled images + video_key: Video observation key for video mode (e.g., 'observation.images.base') + video_batch_size: Number of episodes to process in each VLM batch (video mode only) + + Returns: + Tuple of (tasks_df, task_indices_array, debug_outputs) + - tasks_df: DataFrame with high-level tasks (user_prompt, robot_utterance, etc.) + - task_indices_array: Array of task indices for each frame (full dataset length) + - debug_outputs: List of debug dictionaries (only for sampled frames) + """ + if console is None: + console = Console() + + # Extract metadata + coarse_description = skills_metadata.get("coarse_description", "Complete the task") + episodes = skills_metadata.get("episodes", {}) + + # Track unique high-level tasks + high_level_tasks = {} # (user_prompt, robot_utterance, skill) -> task_index + task_index_counter = 0 # Start at 0 + + # Array to store task index for each frame - MUST match full dataset length + full_dataset_length = len(dataset) + task_indices = np.zeros(full_dataset_length, dtype=np.int64) + + # For debugging - save to JSONL + debug_outputs = [] + + # Initialize video extractor if in video mode + video_extractor = VideoExtractor(console) if video_mode else None + + if video_mode: + if video_key is None: + raise ValueError("video_key must be provided when video_mode=True") + console.print(f"[cyan]Using VIDEO MODE with video key: {video_key}[/cyan]") + console.print(f"[cyan]Video batch size: {video_batch_size} episodes per VLM call[/cyan]") + # In video mode, process episodes in batches with full videos + return _generate_synthetic_data_video_mode( + dataset=dataset, + pgen=pgen, + skills_metadata=skills_metadata, + video_key=video_key, + video_extractor=video_extractor, + console=console, + sample_interval_seconds=sample_interval_seconds, + batch_size=video_batch_size, + ) + + # IMAGE MODE (original logic) + # Track sampling + last_sample_timestamp = {} # episode_idx -> last sampled timestamp + last_task_index = {} # episode_idx -> last generated task_index + frames_sampled = 0 + + console.print(f"[cyan]Processing all {full_dataset_length} frames from {dataset.meta.total_episodes} episodes...[/cyan]") + console.print(f"[cyan]Sampling interval: {sample_interval_seconds}s (fps: {dataset.meta.fps})[/cyan]") + + # Process each frame in the FULL dataset + for frame_idx in tqdm(range(full_dataset_length), desc="Generating synthetic dialogue"): + try: + # Get frame data + frame = dataset[frame_idx] + episode_idx = frame["episode_index"].item() + timestamp = frame["timestamp"].item() + + # Get episode skills + episode_key = str(episode_idx) + if episode_key not in episodes: + console.print(f"[yellow]Warning: Episode {episode_idx} not in skills metadata[/yellow]") + continue + + episode_data = episodes[episode_key] + skills = episode_data.get("skills", []) + description = episode_data.get("description", coarse_description) + + # Find current skill + current_skill = get_skill_at_timestamp(skills, timestamp) + if current_skill is None: + console.print(f"[yellow]Warning: No skill found for timestamp {timestamp}[/yellow]") + continue + + # Determine if we should sample this frame + should_sample = False + + # Always sample first frame of an episode + if episode_idx not in last_sample_timestamp: + should_sample = True + last_sample_timestamp[episode_idx] = timestamp + else: + # Sample if enough time has passed + time_since_last = timestamp - last_sample_timestamp[episode_idx] + if time_since_last >= sample_interval_seconds: + should_sample = True + last_sample_timestamp[episode_idx] = timestamp + + # If not sampling, reuse last task index for this episode + if not should_sample: + if episode_idx in last_task_index: + task_indices[frame_idx] = last_task_index[episode_idx] + continue + + # Sample this frame - generate synthetic dialogue + frames_sampled += 1 + + # Build skill history (all skills before current timestamp) + skill_history = [] + for skill in skills: + if skill["end"] <= timestamp: + skill_history.append(skill["name"]) + + # Load images + images = [] + for img_key in image_keys: + if img_key in frame: + # Frame images are tensors (C, H, W) in [0, 1] + img_tensor = frame[img_key] + if len(img_tensor.shape) == 4: # (T, C, H, W) + img_tensor = img_tensor[-1] # Take last frame + + # Convert to PIL Image + img_array = (img_tensor.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8) + img_pil = Image.fromarray(img_array) + images.append(img_pil) + + if not images: + console.print(f"[yellow]Warning: No images found for frame {frame_idx}[/yellow]") + continue + + # Generate synthetic dialogue + result = annotate_sample_image( + pgen=pgen, + images=images, + task_description=description, + skill_history=skill_history, + skill_current=current_skill, + ) + + # Create unique task key + task_key = ( + result["user_prompt"], + result["robot_utterance"], + current_skill, + result["scenario_type"], + result["response_type"], + ) + + # Assign or create task index + if task_key not in high_level_tasks: + high_level_tasks[task_key] = task_index_counter + task_index_counter += 1 + + current_task_idx = high_level_tasks[task_key] + task_indices[frame_idx] = current_task_idx + last_task_index[episode_idx] = current_task_idx + + # Save for debugging + debug_outputs.append({ + "episode_id": int(episode_idx), + "frame_index": frame_idx, + "timestamp": float(timestamp), + "skill_current": current_skill, + "skill_history": skill_history, + "task_description": description, + "sampled": True, + **result, + }) + + except Exception as e: + console.print(f"[red]Error processing frame {frame_idx}: {e}[/red]") + continue + + console.print(f"[green]✓ Sampled {frames_sampled} frames out of {full_dataset_length} total ({frames_sampled/full_dataset_length*100:.1f}%)[/green]") + + # Create tasks DataFrame + tasks_data = [] + for task_key, task_idx in sorted(high_level_tasks.items(), key=lambda x: x[1]): + user_prompt, robot_utterance, skill, scenario_type, response_type = task_key + tasks_data.append({ + "task": f"{user_prompt} | {robot_utterance}", + "task_index": task_idx, + "user_prompt": user_prompt, + "robot_utterance": robot_utterance, + "skill": skill, + "scenario_type": scenario_type, + "response_type": response_type, + }) + + tasks_df = pd.DataFrame(tasks_data).set_index("task") + + console.print(f"[green]✓ Generated {len(high_level_tasks)} unique high-level tasks[/green]") + + return tasks_df, task_indices, debug_outputs + + +def save_high_level_tasks( + tasks_df: pd.DataFrame, + dataset_root: Path, + console: Console | None = None, +) -> None: + """Save high-level tasks to tasks_high_level.parquet.""" + if console is None: + console = Console() + + output_path = dataset_root / "meta" / "tasks_high_level.parquet" + output_path.parent.mkdir(parents=True, exist_ok=True) + + tasks_df.to_parquet(output_path, engine="pyarrow", compression="snappy") + console.print(f"[green]✓ Saved high-level tasks to {output_path}[/green]") + + +def save_debug_outputs( + debug_outputs: list[dict], + dataset_root: Path, + console: Console | None = None, +) -> None: + """Save debug outputs to JSONL file.""" + if console is None: + console = Console() + + output_path = dataset_root / "meta" / "syn_annotations.jsonl" + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w") as f: + for item in debug_outputs: + f.write(json.dumps(item) + "\n") + + console.print(f"[green]✓ Saved debug annotations to {output_path}[/green]") + + +# main entry point + +def main(): + """Main entry point for synthetic data generation.""" + parser = argparse.ArgumentParser( + description="Generate synthetic dialogue data for hierarchical robot policies", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("""\ + Examples: + # Generate synthetic data for a dataset (image mode) + python annotate_pgen.py --repo-id lerobot/svla_so101_pickplace \\ + --model Qwen/Qwen2-VL-7B-Instruct \\ + --output-dir ./output + + # Use video mode with batching (passes full episode videos) + python annotate_pgen.py --repo-id lerobot/svla_so101_pickplace \\ + --model Qwen/Qwen2-VL-7B-Instruct \\ + --video-mode \\ + --video-key observation.images.base \\ + --video-batch-size 4 + + # Use Qwen3 model with custom parameters + python annotate_pgen.py --repo-id lerobot/svla_so101_pickplace \\ + --model Qwen/Qwen3-VL-30B-A3B-Instruct \\ + --temperature 0.8 \\ + --batch-size 1 + """), + ) + + # Data source + data_group = parser.add_mutually_exclusive_group(required=True) + data_group.add_argument("--data-dir", type=str, help="Path to local LeRobot dataset") + data_group.add_argument("--repo-id", type=str, help="HuggingFace Hub dataset repository ID") + + # Model configuration + parser.add_argument( + "--model", + type=str, + default="Qwen/Qwen2-VL-7B-Instruct", + help="VLM model to use (default: Qwen/Qwen2-VL-7B-Instruct)", + ) + parser.add_argument( + "--device", + type=str, + default="cuda", + help="Device to run model on (default: cuda)", + ) + parser.add_argument( + "--dtype", + type=str, + default="bfloat16", + choices=["bfloat16", "float16", "float32"], + help="Model dtype (default: bfloat16)", + ) + parser.add_argument( + "--temperature", + type=float, + default=0.7, + help="Sampling temperature (default: 0.7)", + ) + + # Processing options + parser.add_argument( + "--batch-size", + type=int, + default=1, + help="Batch size for processing (default: 1) [currently unused]", + ) + parser.add_argument( + "--num-image-views-per-sample", + type=int, + default=1, + help="Number of camera views to use per sample (default: 1)", + ) + parser.add_argument( + "--sample-interval", + type=float, + default=1.0, + help="Generate dialogue every N seconds (default: 1.0). Frames between samples reuse the last generated dialogue. " + "Use larger intervals (e.g., 2.0 or 5.0) for faster processing during testing.", + ) + parser.add_argument( + "--video-mode", + action="store_true", + help="Use video input instead of sampled image frames. Passes entire skill video clips to the model.", + ) + parser.add_argument( + "--video-key", + type=str, + default=None, + help="Video observation key for video mode (e.g., 'observation.images.base'). " + "If not specified, uses the first available video key.", + ) + parser.add_argument( + "--video-batch-size", + type=int, + default=1, + help="Number of episodes to process in each VLM batch call in video mode (default: 1)", + ) + + # Output options + parser.add_argument( + "--output-dir", + type=str, + default=None, + help="Output directory for modified dataset", + ) + parser.add_argument( + "--push-to-hub", + action="store_true", + help="Push modified dataset to HuggingFace Hub", + ) + # add image key + parser.add_argument( + "--image-key", + type=str, + default=None, + help="Image observation key to use for image mode (default: None)", + ) + + args = parser.parse_args() + console = Console() + + # Load dataset + console.print("[cyan]Loading dataset...[/cyan]") + if args.data_dir: + dataset = LeRobotDataset(repo_id="local/dataset", root=args.data_dir) + dataset_root = Path(args.data_dir) + else: + dataset = LeRobotDataset(repo_id=args.repo_id) + dataset_root = dataset.root + + console.print(f"[green]✓ Loaded dataset with {len(dataset)} frames[/green]") + + # Load skills metadata + console.print("[cyan]Loading skills metadata...[/cyan]") + skills_metadata = load_skills_metadata(dataset_root) + if skills_metadata is None: + console.print("[red]Error: No skills.json found. Run annotate.py first![/red]") + return + + console.print(f"[green]✓ Loaded skills for {len(skills_metadata.get('episodes', {}))} episodes[/green]") + + # Initialize model + dtype_map = { + "bfloat16": torch.bfloat16, + "float16": torch.float16, + "float32": torch.float32, + } + torch_dtype = dtype_map[args.dtype] + + console.print(f"[cyan]Initializing {args.model}...[/cyan]") + pgen = QwenPgen( + model_name=args.model, + device=args.device, + torch_dtype=torch_dtype, + temperature=args.temperature, + ) + + # Get image keys (for image mode) + if args.image_key: + image_keys = [args.image_key] + else: + image_keys = dataset.meta.camera_keys[:args.num_image_views_per_sample] + if not args.video_mode: + console.print(f"[cyan]Using image keys: {image_keys}[/cyan]") + + # Determine video key for video mode + video_key = None + if args.video_mode: + if args.video_key: + # Use explicitly provided video key + video_key = args.video_key + if video_key not in dataset.meta.video_keys: + console.print(f"[red]Error: Video key '{video_key}' not found in dataset.[/red]") + console.print(f"[yellow]Available video keys: {', '.join(dataset.meta.video_keys)}[/yellow]") + return + elif dataset.meta.video_keys: + # Use first available video key + video_key = dataset.meta.video_keys[0] + else: + console.print("[red]Error: No video keys found in dataset. Cannot use video mode.[/red]") + return + console.print(f"[cyan]Using video key for video mode: {video_key}[/cyan]") + + # Generate synthetic data + tasks_df, task_indices, debug_outputs = generate_synthetic_data( + dataset=dataset, + pgen=pgen, + skills_metadata=skills_metadata, + image_keys=image_keys, + sample_interval_seconds=args.sample_interval, + console=console, + video_mode=args.video_mode, + video_key=video_key, + video_batch_size=args.video_batch_size, + ) + + # Save high-level tasks + save_high_level_tasks(tasks_df, dataset_root, console) + save_debug_outputs(debug_outputs, dataset_root, console) + + # Add task_index_high_level feature to dataset + console.print("[cyan]Adding task_index_high_level feature to dataset...[/cyan]") + + # Determine output directory + if args.output_dir: + output_dir = Path(args.output_dir) + repo_id = f"{dataset.repo_id}_with_high_level_tasks" + else: + output_dir = None + repo_id = f"{dataset.repo_id}_with_high_level_tasks" + + # Add feature using dataset_tools + feature_info = { + "dtype": "int64", + "shape": (1,), + "names": None, + } + new_dataset = add_features( + dataset=dataset, + features={ + "task_index_high_level": (task_indices, feature_info), + }, + output_dir=output_dir, + repo_id=repo_id, + ) + + # copy high level tsk parquet to new output directory + import shutil + shutil.copy(dataset_root / "meta" / "tasks_high_level.parquet", output_dir / "meta" / "tasks_high_level.parquet") + shutil.copy(dataset_root / "meta" / "syn_annotations.jsonl", output_dir / "meta" / "syn_annotations.jsonl") + + console.print(f"[bold green]✓ Successfully added task_index_high_level feature![/bold green]") + console.print(f" New dataset saved to: {new_dataset.root}") + console.print(f" Total high-level tasks: {len(tasks_df)}") + + # Push to hub if requested + if args.push_to_hub: + if args.data_dir: + console.print("[yellow]Warning: --push-to-hub requires --repo-id, skipping...[/yellow]") + else: + console.print("[cyan]Pushing to HuggingFace Hub...[/cyan]") + try: + new_dataset.push_to_hub() + console.print(f"[green]✓ Pushed to {repo_id}[/green]") + except Exception as e: + console.print(f"[red]Push failed: {e}[/red]") + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/data_processing/annotations/load_lerobot_high.py b/src/lerobot/data_processing/annotations/load_lerobot_high.py new file mode 100644 index 000000000..1b5ab30f5 --- /dev/null +++ b/src/lerobot/data_processing/annotations/load_lerobot_high.py @@ -0,0 +1,52 @@ +import torch +from huggingface_hub import HfApi + +import lerobot +from lerobot.datasets.lerobot_dataset import LeRobotDataset, LeRobotDatasetMetadata +from lerobot.policies.factory import make_pre_post_processors +from lerobot.configs.policies import PreTrainedConfig + +# /fsx/jade_choghari/data/libero_10_subtasks_kw_converted +dataset = LeRobotDataset(repo_id="lerobot/libero_10_image_subtask") + +dataloader = torch.utils.data.DataLoader( + dataset, + num_workers=0, + batch_size=2, + shuffle=True, +) + +cfg = PreTrainedConfig.from_pretrained( + pretrained_name_or_path="/fsx/jade_choghari/models/pi05-base", +) +cfg.dtype = "bfloat16" + +pre_processor, post_processor = make_pre_post_processors( + policy_cfg=cfg, + pretrained_path="/fsx/jade_choghari/models/pi05-base", +) +batch = next(iter(dataloader)) +breakpoint() +batch1 = pre_processor(batch) +breakpoint() +print(batch.keys()) +# print(batch['task_index_high_level'].shape) +# print(batch['task_index_high_level']) +# print(batch['user_prompt'][0]) +# print(batch['robot_utterance'][0]) +# print(batch['task'][0]) + +valid_episode_list = [] +for episode_idx in range(len(dataset.meta.episodes)): + subtask_index = dataset[episode_idx]["subtask_index"] + valid_episode_list.append(episode_idx) + +print(len(valid_episode_list)) + +# read this parquet /fsx/jade_choghari/outputs/pgen_annotations1/meta/tasks.parquett +# import pandas as pd +# tasks_df = pd.read_parquet('/fsx/jade_choghari/outputs/pgen_annotations1/meta/tasks.parquet') + +# # print all +# print(tasks_df.columns) +# breakpoint() \ No newline at end of file diff --git a/src/lerobot/data_processing/annotations/run_pgen.sh b/src/lerobot/data_processing/annotations/run_pgen.sh new file mode 100644 index 000000000..7250d8bf3 --- /dev/null +++ b/src/lerobot/data_processing/annotations/run_pgen.sh @@ -0,0 +1,49 @@ +#!/bin/bash + +# Example script to run synthetic data generation with Qwen VLM +# This generates user prompts and robot utterances for hierarchical policy training + +# Configuration +REPO_ID="jadechoghari/collect-data" +MODEL="Qwen/Qwen3-VL-30B-A3B-Instruct" +# or: MODEL="Qwen/Qwen2-VL-7B-Instruct" + + +OUTPUT_DIR="/fsx/jade_choghari/outputs/collect-data-pgen_new" + +BATCH_SIZE=32 +TEMPERATURE=0.9 +SAMPLE_INTERVAL=5.0 # generate dialogue every 1 second (all episodes processed) + +# Run subtask annotation +python /admin/home/jade_choghari/lerobot/src/lerobot/policies/pi05_full/annotate/subtask_annotate.py \ + --repo-id "$REPO_ID" \ + --video-key observation.images.base \ + --output-dir "$OUTPUT_DIR" \ + --output-repo-id "jadechoghari/collect-data-with-subtasks" +# run synthetic data generation (all episodes processed) +# python examples/dataset/annotate_pgen.py \ +# --repo-id "$REPO_ID" \ +# --model "$MODEL" \ +# --output-dir "$OUTPUT_DIR" \ +# --temperature "$TEMPERATURE" \ +# --batch-size "$BATCH_SIZE" \ +# --sample-interval "$SAMPLE_INTERVAL" \ +# --image-key observation.images.base \ +# --num-image-views-per-sample 1 + +# for faster testing, increase sample interval: +# --sample-interval 5.0 # Samples every 5 seconds (much faster) + +# to push to hub after generation: +# add --push-to-hub flag + +# efficient batch processing: 4 episodes at once +# python examples/dataset/annotate_pgen.py \ +# --repo-id "$REPO_ID" \ +# --model "$MODEL" \ +# --output-dir "$OUTPUT_DIR" \ +# --video-mode \ +# --video-key observation.images.up \ +# --video-batch-size "$BATCH_SIZE" \ +# --sample-interval 1.0 diff --git a/src/lerobot/data_processing/annotations/subtask_annotate.py b/src/lerobot/data_processing/annotations/subtask_annotate.py new file mode 100644 index 000000000..73d763e9c --- /dev/null +++ b/src/lerobot/data_processing/annotations/subtask_annotate.py @@ -0,0 +1,1356 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Automatic Skill Annotation for LeRobot Datasets. + +This script performs automatic subtask/skill labeling for ANY LeRobot dataset using +Vision-Language Models (VLMs). It segments each robot demonstration into short atomic +skills (1-3 seconds each) and creates a new dataset with subtask annotations. + +The pipeline: +1. Loads a LeRobot dataset (local or from HuggingFace Hub) +2. For each episode, extracts video frames +3. Uses a VLM to identify skill boundaries and labels +4. Creates a subtasks.parquet file with unique subtasks +5. Adds a subtask_index feature to the dataset + +NOTE: This script does NOT modify the original tasks.parquet file. It creates a +separate subtask hierarchy while preserving the original task annotations. + +Supported VLMs (modular design allows easy extension): +- Qwen2-VL (default): "Qwen/Qwen2-VL-7B-Instruct" +- Qwen3-VL: "Qwen/Qwen3-VL-30B-A3B-Instruct" + +Usage: +```bash +python examples/dataset/annotate.py \ + --repo-id your-username/your-dataset \ + --video-key observation.images.base \ + --model Qwen/Qwen2-VL-7B-Instruct \ + --output-dir /path/to/output \ + --push-to-hub +``` + +Or with a local dataset: +```bash +python examples/dataset/annotate.py \ + --data-dir /path/to/local/dataset \ + --video-key observation.images.base \ + --output-dir /path/to/output +``` + +After running, you can access the subtask for any frame via: +```python +dataset = LeRobotDataset(repo_id="your/dataset_with_subtasks") +item = dataset[100] +subtask_idx = item["subtask_index"] +subtask_name = dataset.meta.subtasks.iloc[subtask_idx.item()].name +``` +""" + +import argparse +import json +import re +import subprocess +import tempfile +import textwrap +from abc import ABC, abstractmethod +from pathlib import Path +from typing import Any + +import cv2 +import numpy as np +import pandas as pd +import torch +from rich.console import Console +from rich.progress import Progress, SpinnerColumn, TextColumn + +from lerobot.datasets.dataset_tools import add_features +from lerobot.datasets.lerobot_dataset import LeRobotDataset + + +# Skill Annotation Data Structures + + +class Skill: + """Represents a single atomic skill/subtask in a demonstration.""" + + def __init__(self, name: str, start: float, end: float): + self.name = name + self.start = start # Start timestamp in seconds + self.end = end # End timestamp in seconds + + def to_dict(self) -> dict: + return {"name": self.name, "start": self.start, "end": self.end} + + @classmethod + def from_dict(cls, data: dict) -> "Skill": + return cls(name=data["name"], start=data["start"], end=data["end"]) + + def __repr__(self) -> str: + return f"Skill(name='{self.name}', start={self.start:.2f}, end={self.end:.2f})" + + +class EpisodeSkills: + """Container for all skills in an episode.""" + + def __init__(self, episode_index: int, description: str, skills: list[Skill]): + self.episode_index = episode_index + self.description = description + self.skills = skills + + def to_dict(self) -> dict: + return { + "episode_index": self.episode_index, + "description": self.description, + "skills": [s.to_dict() for s in self.skills], + } + + +# VLM Interface (Abstract Base Class for Modularity) + + +class BaseVLM(ABC): + """ + Abstract base class for Vision-Language Models. + + To add a new VLM: + 1. Create a subclass of BaseVLM + 2. Implement the `__init__`, `segment_skills`, and `segment_skills_batch` methods + 3. Register it in the VLM_REGISTRY dictionary + """ + + @abstractmethod + def __init__(self, model_name: str, device: str = "cuda", torch_dtype: torch.dtype = torch.bfloat16): + """Initialize the VLM with model name, device, and dtype.""" + pass + + @abstractmethod + def segment_skills( + self, video_path: Path, episode_duration: float, coarse_goal: str | None = None + ) -> list[Skill]: + """ + Segment a video into atomic skills. + + Args: + video_path: Path to the video file + episode_duration: Total duration of the episode in seconds + coarse_goal: Optional high-level task description + + Returns: + List of Skill objects representing atomic manipulation skills + """ + pass + + @abstractmethod + def segment_skills_batch( + self, video_paths: list[Path], episode_durations: list[float], coarse_goal: str | None = None + ) -> list[list[Skill]]: + """ + Segment multiple videos into atomic skills in a single batch. + + Args: + video_paths: List of paths to video files + episode_durations: List of episode durations in seconds + coarse_goal: Optional high-level task description + + Returns: + List of skill lists, one for each video + """ + pass + + +def create_skill_segmentation_prompt(coarse_goal: str | None = None) -> str: + """Create the prompt for skill segmentation.""" + goal_context = f'The overall goal is: "{coarse_goal}"\n\n' if coarse_goal else "" + + return textwrap.dedent(f"""\ + # Role + You are a Robotics Vision System specializing in temporal action segmentation for robot manipulation demonstrations. + + # Task + {goal_context}Segment this robot demonstration video into short atomic manipulation skills. Each skill should: + - Last approximately 1-3 seconds + - Describe a clear, single action (e.g., "pick up object", "move arm left", "release gripper") + - Have precise start and end timestamps + + # Requirements + 1. **Atomic Actions**: Each skill should be a single, indivisible action + 2. **Complete Coverage**: Skills must cover the entire video duration with no gaps + 3. **Boundary Consistency**: The end of one skill equals the start of the next + 4. **Natural Language**: Use clear, descriptive names for each skill + 5. **Timestamps**: Use seconds (float) for all timestamps + + + + # Output Format + After your analysis, output ONLY valid JSON with this exact structure: + + ```json + {{ + "skills": [ + {{"name": "skill description", "start": 0.0, "end": 1.5}}, + {{"name": "another skill", "start": 1.5, "end": 3.2}} + ] + }} + ``` + + The first skill must start at 0.0 and the last skill must end at the video duration. + """) + + +# Qwen2-VL Implementation + + +class Qwen2VL(BaseVLM): + """Qwen2-VL model for skill segmentation.""" + + def __init__(self, model_name: str, device: str = "cuda", torch_dtype: torch.dtype = torch.bfloat16): + from qwen_vl_utils import process_vision_info + from transformers import AutoProcessor, Qwen2VLForConditionalGeneration + + self.console = Console() + self.device = device + self.model_name = model_name + self.process_vision_info = process_vision_info + + self.console.print(f"[cyan]Loading Qwen2-VL model: {model_name}...[/cyan]") + + self.model = Qwen2VLForConditionalGeneration.from_pretrained( + model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True + ) + self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) + + self.console.print(f"[green]✓ Model loaded successfully on {device}[/green]") + + def segment_skills( + self, video_path: Path, episode_duration: float, coarse_goal: str | None = None + ) -> list[Skill]: + """Segment video into skills using Qwen2-VL.""" + prompt = create_skill_segmentation_prompt(coarse_goal) + duration_str = f"{int(episode_duration // 60):02d}:{int(episode_duration % 60):02d}" + + messages = [ + {"role": "system", "content": [{"type": "text", "text": prompt}]}, + { + "role": "user", + "content": [ + {"type": "video", "video": str(video_path), "fps": 1.0}, + { + "type": "text", + "text": f"Video duration: {duration_str} (~{episode_duration:.1f}s). Segment into atomic skills.", + }, + ], + }, + ] + + text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + image_inputs, video_inputs = self.process_vision_info(messages) + inputs = self.processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ).to(self.device) + + with torch.no_grad(): + generated_ids = self.model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7) + + response = self.processor.batch_decode( + [out[len(inp) :] for inp, out in zip(inputs.input_ids, generated_ids)], + skip_special_tokens=True, + )[0].strip() + + return self._parse_skills_response(response) + + def segment_skills_batch( + self, video_paths: list[Path], episode_durations: list[float], coarse_goal: str | None = None + ) -> list[list[Skill]]: + """Segment multiple videos into skills using Qwen2-VL in a batch.""" + prompt = create_skill_segmentation_prompt(coarse_goal) + + # Create messages for each video + all_messages = [] + for video_path, duration in zip(video_paths, episode_durations): + duration_str = f"{int(duration // 60):02d}:{int(duration % 60):02d}" + messages = [ + {"role": "system", "content": [{"type": "text", "text": prompt}]}, + { + "role": "user", + "content": [ + {"type": "video", "video": str(video_path), "fps": 1.0}, + { + "type": "text", + "text": f"Video duration: {duration_str} (~{duration:.1f}s). Segment into atomic skills.", + }, + ], + }, + ] + all_messages.append(messages) + + # Process all videos in batch + all_texts = [] + all_image_inputs = [] + all_video_inputs = [] + + for messages in all_messages: + text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + image_inputs, video_inputs = self.process_vision_info(messages) + all_texts.append(text) + all_image_inputs.extend(image_inputs or []) + all_video_inputs.extend(video_inputs or []) + + inputs = self.processor( + text=all_texts, + images=all_image_inputs if all_image_inputs else None, + videos=all_video_inputs if all_video_inputs else None, + padding=True, + return_tensors="pt", + ).to(self.device) + + with torch.no_grad(): + generated_ids = self.model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7) + + responses = self.processor.batch_decode( + [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)], + skip_special_tokens=True, + ) + + # Parse each response + all_skills = [] + for idx, response in enumerate(responses): + try: + skills = self._parse_skills_response(response.strip()) + if not skills: + self.console.print(f"[yellow]Warning: No skills parsed from response for video {idx}[/yellow]") + all_skills.append(skills) + except Exception as e: + self.console.print(f"[yellow]Warning: Failed to parse response for video {idx}: {e}[/yellow]") + all_skills.append([]) + + return all_skills + + def _parse_skills_response(self, response: str) -> list[Skill]: + """Parse the VLM response into Skill objects.""" + # Extract JSON from response + if "```json" in response: + response = response.split("```json")[1].split("```")[0] + elif "```" in response: + response = response.split("```")[1].split("```")[0] + + try: + data = json.loads(response) + skills_data = data.get("skills", data) + if isinstance(skills_data, list): + return [Skill.from_dict(s) for s in skills_data] + except json.JSONDecodeError: + # Try to find JSON object in response + match = re.search(r"\{.*\}", response, re.DOTALL) + if match: + data = json.loads(match.group()) + skills_data = data.get("skills", []) + return [Skill.from_dict(s) for s in skills_data] + + raise ValueError(f"Could not parse skills from response: {response[:200]}...") + + +# Qwen3-VL Implementation (MoE variant) + + +class Qwen3VL(BaseVLM): + """Qwen3-VL MoE model for skill segmentation.""" + + def __init__(self, model_name: str, device: str = "cuda", torch_dtype: torch.dtype = torch.bfloat16): + from qwen_vl_utils import process_vision_info + from transformers import AutoProcessor, Qwen3VLMoeForConditionalGeneration + + self.console = Console() + self.device = device + self.model_name = model_name + self.process_vision_info = process_vision_info + + self.console.print(f"[cyan]Loading Qwen3-VL model: {model_name}...[/cyan]") + + self.model = Qwen3VLMoeForConditionalGeneration.from_pretrained( + model_name, torch_dtype=torch_dtype, device_map=device, trust_remote_code=True + ) + self.processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) + + self.console.print(f"[green]✓ Model loaded successfully on {device}[/green]") + + def segment_skills( + self, video_path: Path, episode_duration: float, coarse_goal: str | None = None + ) -> list[Skill]: + """Segment video into skills using Qwen3-VL.""" + prompt = create_skill_segmentation_prompt(coarse_goal) + duration_str = f"{int(episode_duration // 60):02d}:{int(episode_duration % 60):02d}" + + messages = [ + {"role": "system", "content": [{"type": "text", "text": prompt}]}, + { + "role": "user", + "content": [ + {"type": "video", "video": str(video_path), "fps": 1.0}, + { + "type": "text", + "text": f"Video duration: {duration_str} (~{episode_duration:.1f}s). Segment into atomic skills.", + }, + ], + }, + ] + + text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + image_inputs, video_inputs = self.process_vision_info(messages) + inputs = self.processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ).to(self.device) + + with torch.no_grad(): + generated_ids = self.model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7) + + response = self.processor.batch_decode( + [out[len(inp) :] for inp, out in zip(inputs.input_ids, generated_ids)], + skip_special_tokens=True, + )[0].strip() + + return self._parse_skills_response(response) + + def segment_skills_batch( + self, video_paths: list[Path], episode_durations: list[float], coarse_goal: str | None = None + ) -> list[list[Skill]]: + """Segment multiple videos into skills using Qwen3-VL in a batch.""" + prompt = create_skill_segmentation_prompt(coarse_goal) + + # Create messages for each video + all_messages = [] + for video_path, duration in zip(video_paths, episode_durations): + duration_str = f"{int(duration // 60):02d}:{int(duration % 60):02d}" + messages = [ + {"role": "system", "content": [{"type": "text", "text": prompt}]}, + { + "role": "user", + "content": [ + {"type": "video", "video": str(video_path), "fps": 1.0}, + { + "type": "text", + "text": f"Video duration: {duration_str} (~{duration:.1f}s). Segment into atomic skills.", + }, + ], + }, + ] + all_messages.append(messages) + + # Process all videos in batch + all_texts = [] + all_image_inputs = [] + all_video_inputs = [] + + for messages in all_messages: + text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + image_inputs, video_inputs = self.process_vision_info(messages) + all_texts.append(text) + all_image_inputs.extend(image_inputs or []) + all_video_inputs.extend(video_inputs or []) + + inputs = self.processor( + text=all_texts, + images=all_image_inputs if all_image_inputs else None, + videos=all_video_inputs if all_video_inputs else None, + padding=True, + return_tensors="pt", + ).to(self.device) + + with torch.no_grad(): + generated_ids = self.model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7) + + responses = self.processor.batch_decode( + [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)], + skip_special_tokens=True, + ) + + # Parse each response + all_skills = [] + for idx, response in enumerate(responses): + try: + skills = self._parse_skills_response(response.strip()) + if not skills: + self.console.print(f"[yellow]Warning: No skills parsed from response for video {idx}[/yellow]") + all_skills.append(skills) + except Exception as e: + self.console.print(f"[yellow]Warning: Failed to parse response for video {idx}: {e}[/yellow]") + all_skills.append([]) + + return all_skills + + def _parse_skills_response(self, response: str) -> list[Skill]: + """Parse the VLM response into Skill objects.""" + if "```json" in response: + response = response.split("```json")[1].split("```")[0] + elif "```" in response: + response = response.split("```")[1].split("```")[0] + + try: + data = json.loads(response) + skills_data = data.get("skills", data) + if isinstance(skills_data, list): + return [Skill.from_dict(s) for s in skills_data] + except json.JSONDecodeError: + match = re.search(r"\{.*\}", response, re.DOTALL) + if match: + data = json.loads(match.group()) + skills_data = data.get("skills", []) + return [Skill.from_dict(s) for s in skills_data] + + raise ValueError(f"Could not parse skills from response: {response[:200]}...") + + +# VLM Registry - Add new VLMs here + +VLM_REGISTRY: dict[str, type[BaseVLM]] = { + # Qwen2-VL variants + "Qwen/Qwen2-VL-2B-Instruct": Qwen2VL, + "Qwen/Qwen2-VL-7B-Instruct": Qwen2VL, + "Qwen/Qwen2-VL-72B-Instruct": Qwen2VL, + # Qwen3-VL variants (MoE) + "Qwen/Qwen3-VL-30B-A3B-Instruct": Qwen3VL, +} + + +def get_vlm(model_name: str, device: str = "cuda", torch_dtype: torch.dtype = torch.bfloat16) -> BaseVLM: + """ + Factory function to get the appropriate VLM based on model name. + + Args: + model_name: HuggingFace model identifier + device: Device to load model on + torch_dtype: Data type for model weights + + Returns: + Initialized VLM instance + + Raises: + ValueError: If model is not in registry + """ + # Check exact match first + if model_name in VLM_REGISTRY: + return VLM_REGISTRY[model_name](model_name, device, torch_dtype) + + # Check for partial matches (e.g., "qwen2" in model name) + model_lower = model_name.lower() + if "qwen3" in model_lower: + return Qwen3VL(model_name, device, torch_dtype) + elif "qwen2" in model_lower or "qwen-vl" in model_lower: + return Qwen2VL(model_name, device, torch_dtype) + + raise ValueError( + f"Unknown model: {model_name}. " + f"Supported models: {list(VLM_REGISTRY.keys())}. " + "Or implement a new VLM class inheriting from BaseVLM." + ) + + +# Video Extraction Utilities + +class VideoExtractor: + """Utilities for extracting and processing video segments from LeRobot datasets.""" + + def __init__(self, console: Console | None = None): + self.console = console or Console() + + def extract_episode_video( + self, + video_path: Path, + start_timestamp: float, + end_timestamp: float, + target_fps: int = 1, + ) -> Path: + """ + Extract a specific episode segment from a concatenated video file. + + Args: + video_path: Path to the source video file + start_timestamp: Start time in seconds + end_timestamp: End time in seconds + target_fps: Target frames per second for output + + Returns: + Path to the extracted temporary video file + """ + tmp_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) + tmp_path = Path(tmp_file.name) + tmp_file.close() + + duration = end_timestamp - start_timestamp + + self.console.print( + f"[cyan]Extracting: {start_timestamp:.1f}s - {end_timestamp:.1f}s ({duration:.1f}s)[/cyan]" + ) + + cmd = [ + "ffmpeg", + "-i", + str(video_path), + "-ss", + str(start_timestamp), + "-t", + str(duration), + "-r", + str(target_fps), + "-c:v", + "libx264", + "-preset", + "ultrafast", + "-crf", + "23", + "-an", + "-y", + str(tmp_path), + ] + + try: + subprocess.run(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True) + except subprocess.CalledProcessError as e: + raise RuntimeError(f"FFmpeg failed: {e}") from e + except FileNotFoundError: + raise RuntimeError("FFmpeg not found. Please install ffmpeg.") + + if not tmp_path.exists() or tmp_path.stat().st_size < 1024: + if tmp_path.exists(): + tmp_path.unlink() + raise RuntimeError("Video extraction produced invalid file") + + return tmp_path + + def get_video_duration(self, video_path: Path) -> float: + """Get duration of a video file in seconds.""" + cap = cv2.VideoCapture(str(video_path)) + fps = cap.get(cv2.CAP_PROP_FPS) or 30 + frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + cap.release() + return frame_count / fps + + +# Skill Annotation Pipeline +class SkillAnnotator: + """ + Main class for annotating LeRobot datasets with skill labels. + + This class orchestrates the full annotation pipeline: + 1. Load dataset + 2. Extract video segments for each episode + 3. Run VLM-based skill segmentation + 4. Update dataset task metadata + """ + + def __init__( + self, + vlm: BaseVLM, + video_extractor: VideoExtractor | None = None, + console: Console | None = None, + batch_size: int = 8, + ): + self.vlm = vlm + self.console = console or Console() + self.video_extractor = video_extractor or VideoExtractor(self.console) + self.batch_size = batch_size + + def annotate_dataset( + self, + dataset: LeRobotDataset, + video_key: str, + episodes: list[int] | None = None, + skip_existing: bool = False, + ) -> dict[int, EpisodeSkills]: + """ + Annotate all episodes in a dataset with skill labels using batched processing. + + Args: + dataset: LeRobot dataset to annotate + video_key: Key for video observations (e.g., "observation.images.base") + episodes: Specific episode indices to annotate (None = all) + skip_existing: Skip episodes that already have skill annotations + + Returns: + Dictionary mapping episode index to EpisodeSkills + """ + episode_indices = episodes or list(range(dataset.meta.total_episodes)) + annotations: dict[int, EpisodeSkills] = {} + failed_episodes: dict[int, str] = {} # Track failed episodes with error messages + + # Get coarse task description if available + coarse_goal = self._get_coarse_goal(dataset) + + # Filter out episodes that already have annotations if skip_existing is True + if skip_existing: + existing_annotations = load_skill_annotations(dataset.root) + if existing_annotations and "episodes" in existing_annotations: + # Only skip episodes that exist AND have non-empty skills + existing_episode_indices = set() + for idx_str, episode_data in existing_annotations["episodes"].items(): + idx = int(idx_str) + # Check if skills list exists and is not empty + if "skills" in episode_data and episode_data["skills"]: + existing_episode_indices.add(idx) + + original_count = len(episode_indices) + episode_indices = [ep for ep in episode_indices if ep not in existing_episode_indices] + skipped_count = original_count - len(episode_indices) + if skipped_count > 0: + self.console.print(f"[cyan]Skipping {skipped_count} episodes with existing non-empty annotations[/cyan]") + + if not episode_indices: + self.console.print("[yellow]No episodes to annotate (all already annotated)[/yellow]") + return annotations + + print(f"Annotating {len(episode_indices)} episodes in batches of {self.batch_size}...") + + # Process episodes in batches + for batch_start in range(0, len(episode_indices), self.batch_size): + batch_end = min(batch_start + self.batch_size, len(episode_indices)) + batch_episodes = episode_indices[batch_start:batch_end] + + print(f"Processing batch {batch_start//self.batch_size + 1}/{(len(episode_indices) + self.batch_size - 1)//self.batch_size} (episodes {batch_episodes[0]} to {batch_episodes[-1]})...") + + try: + batch_annotations = self._annotate_episodes_batch( + dataset, batch_episodes, video_key, coarse_goal + ) + + for ep_idx in batch_episodes: + if ep_idx in batch_annotations and batch_annotations[ep_idx]: + skills = batch_annotations[ep_idx] + annotations[ep_idx] = EpisodeSkills( + episode_index=ep_idx, + description=coarse_goal, + skills=skills, + ) + self.console.print( + f"[green]✓ Episode {ep_idx}: {len(skills)} skills identified[/green]" + ) + else: + failed_episodes[ep_idx] = "Empty or missing skills from batch processing" + self.console.print(f"[yellow]⚠ Episode {ep_idx}: No skills extracted, will retry[/yellow]") + except Exception as e: + self.console.print(f"[red]✗ Batch failed: {e}. Falling back to single-episode processing...[/red]") + # Fallback: process episodes one by one + for ep_idx in batch_episodes: + try: + skills = self._annotate_episode(dataset, ep_idx, video_key, coarse_goal) + if skills: + annotations[ep_idx] = EpisodeSkills( + episode_index=ep_idx, + description=coarse_goal, + skills=skills, + ) + self.console.print( + f"[green]✓ Episode {ep_idx}: {len(skills)} skills identified[/green]" + ) + else: + failed_episodes[ep_idx] = "Empty skills list from single-episode processing" + self.console.print(f"[yellow]⚠ Episode {ep_idx}: No skills extracted, will retry[/yellow]") + except Exception as ep_error: + failed_episodes[ep_idx] = str(ep_error) + self.console.print(f"[yellow]⚠ Episode {ep_idx} failed: {ep_error}, will retry[/yellow]") + + # Retry failed episodes one more time + if failed_episodes: + self.console.print(f"\n[cyan]Retrying {len(failed_episodes)} failed episodes...[/cyan]") + retry_count = 0 + for ep_idx, error_msg in list(failed_episodes.items()): + self.console.print(f"[cyan]Retry attempt for episode {ep_idx} (previous error: {error_msg})[/cyan]") + try: + skills = self._annotate_episode(dataset, ep_idx, video_key, coarse_goal) + if skills: + annotations[ep_idx] = EpisodeSkills( + episode_index=ep_idx, + description=coarse_goal, + skills=skills, + ) + self.console.print( + f"[green]✓ Episode {ep_idx} (retry): {len(skills)} skills identified[/green]" + ) + del failed_episodes[ep_idx] + retry_count += 1 + else: + self.console.print(f"[red]✗ Episode {ep_idx} (retry): Still no skills extracted[/red]") + except Exception as retry_error: + failed_episodes[ep_idx] = str(retry_error) + self.console.print(f"[red]✗ Episode {ep_idx} (retry) failed: {retry_error}[/red]") + + if retry_count > 0: + self.console.print(f"[green]Successfully recovered {retry_count} episodes on retry[/green]") + + if failed_episodes: + self.console.print(f"\n[red]⚠ Warning: {len(failed_episodes)} episodes still failed after retry:[/red]") + for ep_idx, error_msg in failed_episodes.items(): + self.console.print(f" Episode {ep_idx}: {error_msg}") + + return annotations + + def _get_coarse_goal(self, dataset: LeRobotDataset) -> str: + """Extract or generate the coarse task description.""" + # Try to get from existing task metadata + if dataset.meta.tasks is not None and len(dataset.meta.tasks) > 0: + # Get the first task description + first_task = dataset.meta.tasks.index[0] + if first_task: + return str(first_task) + + return "Perform the demonstrated manipulation task." + + def _annotate_episodes_batch( + self, + dataset: LeRobotDataset, + episode_indices: list[int], + video_key: str, + coarse_goal: str, + ) -> dict[int, list[Skill]]: + """Annotate multiple episodes with skill labels in a batch.""" + # Extract all videos for this batch + extracted_paths = [] + durations = [] + valid_episode_indices = [] + + for ep_idx in episode_indices: + try: + # Get video path and timestamps + video_path = dataset.root / dataset.meta.get_video_file_path(ep_idx, video_key) + + if not video_path.exists(): + self.console.print(f"[yellow]Warning: Video not found for episode {ep_idx}[/yellow]") + continue + + # Get episode timestamps from metadata + ep = dataset.meta.episodes[ep_idx] + start_ts = float(ep[f"videos/{video_key}/from_timestamp"]) + end_ts = float(ep[f"videos/{video_key}/to_timestamp"]) + duration = end_ts - start_ts + + # Extract episode segment to temporary file + extracted_path = self.video_extractor.extract_episode_video( + video_path, start_ts, end_ts, target_fps=1 + ) + + extracted_paths.append(extracted_path) + durations.append(duration) + valid_episode_indices.append(ep_idx) + + except Exception as e: + self.console.print(f"[yellow]Warning: Failed to extract video for episode {ep_idx}: {e}[/yellow]") + continue + + if not extracted_paths: + return {} + + try: + # Run VLM skill segmentation in batch + all_skills = self.vlm.segment_skills_batch(extracted_paths, durations, coarse_goal) + + # Map results back to episode indices + results = {} + for ep_idx, skills in zip(valid_episode_indices, all_skills): + results[ep_idx] = skills + + return results + + finally: + # Clean up all temporary files + for path in extracted_paths: + if path.exists(): + path.unlink() + + def _annotate_episode( + self, + dataset: LeRobotDataset, + episode_index: int, + video_key: str, + coarse_goal: str, + ) -> list[Skill]: + """Annotate a single episode with skill labels.""" + # Get video path and timestamps for this episode + video_path = dataset.root / dataset.meta.get_video_file_path(episode_index, video_key) + + if not video_path.exists(): + raise FileNotFoundError(f"Video not found: {video_path}") + + # Get episode timestamps from metadata + ep = dataset.meta.episodes[episode_index] + start_ts = float(ep[f"videos/{video_key}/from_timestamp"]) + end_ts = float(ep[f"videos/{video_key}/to_timestamp"]) + duration = end_ts - start_ts + + # Extract episode segment to temporary file + extracted_path = self.video_extractor.extract_episode_video( + video_path, start_ts, end_ts, target_fps=1 + ) + + try: + # Run VLM skill segmentation + skills = self.vlm.segment_skills(extracted_path, duration, coarse_goal) + return skills + finally: + # Clean up temporary file + if extracted_path.exists(): + extracted_path.unlink() + + +# Metadata Writer - Updates per-frame task_index based on skills + + +def get_skill_for_timestamp(skills: list[Skill], timestamp: float) -> Skill | None: + """ + Find which skill covers a given timestamp. + + Args: + skills: List of skills with start/end times + timestamp: Frame timestamp in seconds + + Returns: + The Skill that covers this timestamp, or None if not found + """ + for skill in skills: + if skill.start <= timestamp < skill.end: + return skill + # Handle the last frame (end boundary) + if timestamp >= skill.end and skill == skills[-1]: + return skill + return skills[-1] if skills else None # Fallback to last skill + + +def create_subtasks_dataframe( + annotations: dict[int, EpisodeSkills], +) -> tuple[pd.DataFrame, dict[str, int]]: + """ + Create a subtasks DataFrame from skill annotations. + + Args: + annotations: Dictionary of episode skills + + Returns: + Tuple of (subtasks_df, skill_to_subtask_idx mapping) + """ + console = Console() + + # Collect all unique skill names + all_skill_names: set[str] = set() + for episode_skills in annotations.values(): + for skill in episode_skills.skills: + all_skill_names.add(skill.name) + + console.print(f"[cyan]Found {len(all_skill_names)} unique subtasks[/cyan]") + + # Build subtasks DataFrame + subtask_data = [] + for i, skill_name in enumerate(sorted(all_skill_names)): + subtask_data.append({ + "subtask": skill_name, + "subtask_index": i, + }) + + subtasks_df = pd.DataFrame(subtask_data).set_index("subtask") + + # Build skill name to subtask_index mapping + skill_to_subtask_idx = { + skill_name: int(subtasks_df.loc[skill_name, "subtask_index"]) + for skill_name in all_skill_names + } + + return subtasks_df, skill_to_subtask_idx + + +def save_subtasks( + subtasks_df: pd.DataFrame, + dataset_root: Path, + console: Console | None = None, +) -> None: + """Save subtasks to subtasks.parquet.""" + if console is None: + console = Console() + + output_path = dataset_root / "meta" / "subtasks.parquet" + output_path.parent.mkdir(parents=True, exist_ok=True) + + subtasks_df.to_parquet(output_path, engine="pyarrow", compression="snappy") + console.print(f"[green]✓ Saved subtasks to {output_path}[/green]") + + +def create_subtask_index_array( + dataset: LeRobotDataset, + annotations: dict[int, EpisodeSkills], + skill_to_subtask_idx: dict[str, int], +) -> np.ndarray: + """ + Create a subtask_index array for each frame based on skill annotations. + + Args: + dataset: The LeRobot dataset + annotations: Dictionary of episode skills + skill_to_subtask_idx: Mapping from skill name to subtask_index + + Returns: + Array of subtask indices for each frame in the dataset + """ + console = Console() + + # Array to store subtask index for each frame + # Initialize with -1 to indicate unannotated frames + full_dataset_length = len(dataset) + subtask_indices = np.full(full_dataset_length, -1, dtype=np.int64) + + console.print(f"[cyan]Creating subtask_index array for {full_dataset_length} frames...[/cyan]") + + # Assign subtask_index for each annotated episode + for ep_idx, episode_skills in annotations.items(): + skills = episode_skills.skills + + # Get episode frame range + ep = dataset.meta.episodes[ep_idx] + ep_from = ep["dataset_from_index"] + ep_to = ep["dataset_to_index"] + + # Process each frame in the episode + for frame_idx in range(ep_from, ep_to): + frame = dataset[frame_idx] + timestamp = frame["timestamp"].item() + + # Find which skill covers this timestamp + skill = get_skill_for_timestamp(skills, timestamp) + + if skill and skill.name in skill_to_subtask_idx: + subtask_idx = skill_to_subtask_idx[skill.name] + subtask_indices[frame_idx] = subtask_idx + + console.print(f"[green]✓ Created subtask_index array[/green]") + return subtask_indices + + +def save_skill_annotations( + dataset: LeRobotDataset, + annotations: dict[int, EpisodeSkills], + output_dir: Path | None = None, + repo_id: str | None = None, +) -> LeRobotDataset: + """ + Save skill annotations to the dataset by: + 1. Creating a subtasks.parquet file with unique subtasks + 2. Adding a subtask_index feature to the dataset + 3. Saving raw skill annotations as JSON for reference + + This function does NOT modify tasks.parquet - it keeps the original tasks intact + and creates a separate subtask hierarchy. + + Args: + dataset: The LeRobot dataset to annotate + annotations: Dictionary of episode skills + output_dir: Optional directory to save the modified dataset + repo_id: Optional repository ID for the new dataset + + Returns: + New dataset with subtask_index feature added + """ + console = Console() + + if not annotations: + console.print("[yellow]No annotations to save[/yellow]") + return dataset + + # Step 1: Create subtasks DataFrame + console.print("[cyan]Creating subtasks DataFrame...[/cyan]") + subtasks_df, skill_to_subtask_idx = create_subtasks_dataframe(annotations) + + # Step 2: Create subtask_index array for all frames + console.print("[cyan]Creating subtask_index array...[/cyan]") + subtask_indices = create_subtask_index_array(dataset, annotations, skill_to_subtask_idx) + + # Step 3: Save subtasks.parquet to the original dataset root + save_subtasks(subtasks_df, dataset.root, console) + + # Step 4: Save the raw skill annotations as JSON for reference + skills_path = dataset.root / "meta" / "skills.json" + skills_path.parent.mkdir(parents=True, exist_ok=True) + + # Load existing skills data if it exists and is not empty + existing_skills_data = None + if skills_path.exists(): + try: + with open(skills_path, "r") as f: + existing_skills_data = json.load(f) + if existing_skills_data and len(existing_skills_data.get("episodes", {})) > 0: + console.print(f"[cyan]Found existing skills.json with {len(existing_skills_data.get('episodes', {}))} episodes, merging...[/cyan]") + except (json.JSONDecodeError, IOError): + console.print("[yellow]Warning: Could not load existing skills.json, will create new file[/yellow]") + existing_skills_data = None + + # Prepare new annotations + new_episodes = {str(ep_idx): ann.to_dict() for ep_idx, ann in annotations.items()} + + # Merge with existing data if available + if existing_skills_data: + # Preserve existing episodes that are not being updated + merged_episodes = existing_skills_data.get("episodes", {}).copy() + merged_episodes.update(new_episodes) + + # Merge skill_to_subtask_index mappings + merged_skill_to_subtask = existing_skills_data.get("skill_to_subtask_index", {}).copy() + merged_skill_to_subtask.update(skill_to_subtask_idx) + + # Use existing coarse_description if available, otherwise use new one + coarse_desc = existing_skills_data.get("coarse_description", annotations[next(iter(annotations))].description) + + skills_data = { + "coarse_description": coarse_desc, + "skill_to_subtask_index": merged_skill_to_subtask, + "episodes": merged_episodes, + } + console.print(f"[cyan]Updated {len(new_episodes)} episode(s), total episodes in skills.json: {len(merged_episodes)}[/cyan]") + else: + # No existing data, create new + skills_data = { + "coarse_description": annotations[next(iter(annotations))].description, + "skill_to_subtask_index": skill_to_subtask_idx, + "episodes": new_episodes, + } + + with open(skills_path, "w") as f: + json.dump(skills_data, f, indent=2) + + console.print(f"[green]✓ Saved skill annotations to {skills_path}[/green]") + + # Step 5: Add subtask_index feature to dataset using add_features + console.print("[cyan]Adding subtask_index feature to dataset...[/cyan]") + + # Determine output directory and repo_id + if output_dir is None: + output_dir = dataset.root.parent / f"{dataset.root.name}_with_subtasks" + else: + output_dir = Path(output_dir) + + if repo_id is None: + repo_id = f"{dataset.repo_id}_with_subtasks" + + # Add feature using dataset_tools + feature_info = { + "dtype": "int64", + "shape": (1,), + "names": None, + } + new_dataset = add_features( + dataset=dataset, + features={ + "subtask_index": (subtask_indices, feature_info), + }, + output_dir=output_dir, + repo_id=repo_id, + ) + + # Copy subtasks.parquet to new output directory + import shutil + shutil.copy( + dataset.root / "meta" / "subtasks.parquet", + output_dir / "meta" / "subtasks.parquet" + ) + shutil.copy( + dataset.root / "meta" / "skills.json", + output_dir / "meta" / "skills.json" + ) + + console.print(f"[bold green]✓ Successfully added subtask_index feature![/bold green]") + console.print(f" New dataset saved to: {new_dataset.root}") + console.print(f" Total subtasks: {len(subtasks_df)}") + + return new_dataset + + +def load_skill_annotations(dataset_root: Path) -> dict | None: + """Load existing skill annotations from a dataset.""" + skills_path = dataset_root / "meta" / "skills.json" + if skills_path.exists(): + with open(skills_path) as f: + return json.load(f) + return None + + +# Main Entry Point + + +def main(): + """Main entry point for the skill annotation script.""" + parser = argparse.ArgumentParser( + description="Automatic skill annotation for LeRobot datasets using VLMs (with batched processing)", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=textwrap.dedent("""\ + Examples: + # Annotate a HuggingFace Hub dataset + python annotate.py --repo-id user/dataset --video-key observation.images.base \\ + --output-dir ./output + + # Annotate a local dataset with custom batch size + python annotate.py --data-dir /path/to/dataset --video-key observation.images.base \\ + --batch-size 16 --output-dir ./output + + # Use a specific model + python annotate.py --repo-id user/dataset --video-key observation.images.base \\ + --model Qwen/Qwen2-VL-7B-Instruct --output-dir ./output + + # Push annotated dataset to Hub + python annotate.py --repo-id user/dataset --video-key observation.images.base \\ + --output-dir ./output --push-to-hub + """), + ) + + # Data source (mutually exclusive) + data_group = parser.add_mutually_exclusive_group(required=True) + data_group.add_argument("--data-dir", type=str, help="Path to local LeRobot dataset") + data_group.add_argument("--repo-id", type=str, help="HuggingFace Hub dataset repository ID") + + # Required arguments + parser.add_argument( + "--video-key", + type=str, + required=True, + help="Video observation key (e.g., 'observation.images.base')", + ) + + # Model configuration + parser.add_argument( + "--model", + type=str, + default="Qwen/Qwen2-VL-7B-Instruct", + help="VLM model to use for skill segmentation (default: Qwen/Qwen2-VL-7B-Instruct)", + ) + parser.add_argument( + "--device", + type=str, + default="cuda", + help="Device to run model on (default: cuda)", + ) + parser.add_argument( + "--dtype", + type=str, + default="bfloat16", + choices=["bfloat16", "float16", "float32"], + help="Model dtype (default: bfloat16)", + ) + parser.add_argument( + "--batch-size", + type=int, + default=8, + help="Number of episodes to process in each batch (default: 8)", + ) + + # Episode selection + parser.add_argument( + "--episodes", + type=int, + nargs="+", + help="Specific episode indices to annotate (default: all)", + ) + parser.add_argument( + "--skip-existing", + action="store_true", + help="Skip episodes that already have annotations", + ) + + # Output options + parser.add_argument( + "--push-to-hub", + action="store_true", + help="Push annotated dataset to HuggingFace Hub", + ) + parser.add_argument( + "--output-dir", + type=str, + help="Output directory for modified dataset with subtask_index feature", + ) + parser.add_argument( + "--output-repo-id", + type=str, + help="Repository ID for the new dataset (default: original_repo_id_with_subtasks)", + ) + + args = parser.parse_args() + console = Console() + + # Validate arguments + dtype_map = { + "bfloat16": torch.bfloat16, + "float16": torch.float16, + "float32": torch.float32, + } + torch_dtype = dtype_map[args.dtype] + + # Load dataset + console.print("[cyan]Loading dataset...[/cyan]") + if args.data_dir: + dataset = LeRobotDataset(repo_id="local/dataset", root=args.data_dir, download_videos=False) + else: + dataset = LeRobotDataset(repo_id=args.repo_id, download_videos=True) + + console.print(f"[green]✓ Loaded dataset with {dataset.meta.total_episodes} episodes[/green]") + + # Validate video key + if args.video_key not in dataset.meta.video_keys: + available = ", ".join(dataset.meta.video_keys) + console.print(f"[red]Error: Video key '{args.video_key}' not found. Available: {available}[/red]") + return + + # Initialize VLM + console.print(f"[cyan]Initializing VLM: {args.model}...[/cyan]") + vlm = get_vlm(args.model, args.device, torch_dtype) + + # Create annotator and run annotation + annotator = SkillAnnotator(vlm=vlm, console=console, batch_size=args.batch_size) + console.print(f"[cyan]Processing with batch size: {args.batch_size}[/cyan]") + annotations = annotator.annotate_dataset( + dataset=dataset, + video_key=args.video_key, + episodes=args.episodes, + skip_existing=args.skip_existing, + ) + + # Save annotations + output_dir = Path(args.output_dir) if args.output_dir else None + output_repo_id = args.output_repo_id if args.output_repo_id else None + new_dataset = save_skill_annotations(dataset, annotations, output_dir, output_repo_id) + + # Summary + total_skills = sum(len(ann.skills) for ann in annotations.values()) + console.print(f"\n[bold green]✓ Annotation complete![/bold green]") + console.print(f" Episodes annotated: {len(annotations)}") + console.print(f" Total subtasks identified: {total_skills}") + console.print(f" Dataset with subtask_index saved to: {new_dataset.root}") + + # Push to hub if requested + if args.push_to_hub: + if args.data_dir: + console.print("[yellow]Warning: --push-to-hub requires --repo-id, skipping...[/yellow]") + else: + console.print("[cyan]Pushing to HuggingFace Hub...[/cyan]") + try: + new_dataset.push_to_hub(push_videos=False) + console.print(f"[green]✓ Pushed to {output_repo_id or f'{args.repo_id}_with_subtasks'}[/green]") + except Exception as e: + console.print(f"[red]Push failed: {e}[/red]") + + +if __name__ == "__main__": + main() diff --git a/src/lerobot/datasets/aggregate.py b/src/lerobot/datasets/aggregate.py index 94ffe602e..7020545d2 100644 --- a/src/lerobot/datasets/aggregate.py +++ b/src/lerobot/datasets/aggregate.py @@ -116,6 +116,9 @@ def update_meta_data( Adjusts all indices and timestamps to account for previously aggregated data and videos in the destination dataset. + For data file indices, uses the 'src_to_dst' mapping from aggregate_data() + to correctly map source file indices to their destination locations. + Args: df: DataFrame containing the metadata to be updated. dst_meta: Destination dataset metadata. @@ -129,8 +132,50 @@ def update_meta_data( df["meta/episodes/chunk_index"] = df["meta/episodes/chunk_index"] + meta_idx["chunk"] df["meta/episodes/file_index"] = df["meta/episodes/file_index"] + meta_idx["file"] - df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"] - df["data/file_index"] = df["data/file_index"] + data_idx["file"] + + # Update data file indices using source-to-destination mapping + # This is critical for handling datasets that are already results of a merge + data_src_to_dst = data_idx.get("src_to_dst", {}) + if data_src_to_dst: + # Store original indices for lookup + df["_orig_data_chunk"] = df["data/chunk_index"].copy() + df["_orig_data_file"] = df["data/file_index"].copy() + + # Vectorized mapping from (src_chunk, src_file) to (dst_chunk, dst_file) + # This is much faster than per-row iteration for large metadata tables + mapping_index = pd.MultiIndex.from_tuples( + list(data_src_to_dst.keys()), + names=["chunk_index", "file_index"], + ) + mapping_values = list(data_src_to_dst.values()) + mapping_df = pd.DataFrame( + mapping_values, + index=mapping_index, + columns=["dst_chunk", "dst_file"], + ) + + # Construct a MultiIndex for each row based on original data indices + row_index = pd.MultiIndex.from_arrays( + [df["_orig_data_chunk"], df["_orig_data_file"]], + names=["chunk_index", "file_index"], + ) + + # Align mapping to rows; missing keys fall back to the default destination + reindexed = mapping_df.reindex(row_index) + reindexed[["dst_chunk", "dst_file"]] = reindexed[["dst_chunk", "dst_file"]].fillna( + {"dst_chunk": data_idx["chunk"], "dst_file": data_idx["file"]} + ) + + # Assign mapped destination indices back to the DataFrame + df["data/chunk_index"] = reindexed["dst_chunk"].to_numpy() + df["data/file_index"] = reindexed["dst_file"].to_numpy() + + # Clean up temporary columns + df = df.drop(columns=["_orig_data_chunk", "_orig_data_file"]) + else: + # Fallback to simple offset (backward compatibility for single-file sources) + df["data/chunk_index"] = df["data/chunk_index"] + data_idx["chunk"] + df["data/file_index"] = df["data/file_index"] + data_idx["file"] for key, video_idx in videos_idx.items(): # Store original video file indices before updating orig_chunk_col = f"videos/{key}/chunk_index" @@ -146,8 +191,7 @@ def update_meta_data( if src_to_dst: # Map each episode to its correct destination file and apply offset for idx in df.index: - # Convert to Python int to avoid numpy type mismatch in dict lookup - src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"])) + src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"]) # Get destination chunk/file for this source file dst_chunk, dst_file = src_to_dst.get(src_key, (video_idx["chunk"], video_idx["file"])) @@ -163,8 +207,7 @@ def update_meta_data( df[orig_chunk_col] = video_idx["chunk"] df[orig_file_col] = video_idx["file"] for idx in df.index: - # Convert to Python int to avoid numpy type mismatch in dict lookup - src_key = (int(df.at[idx, "_orig_chunk"]), int(df.at[idx, "_orig_file"])) + src_key = (df.at[idx, "_orig_chunk"], df.at[idx, "_orig_file"]) offset = src_to_offset.get(src_key, 0) df.at[idx, f"videos/{key}/from_timestamp"] += offset df.at[idx, f"videos/{key}/to_timestamp"] += offset @@ -262,6 +305,10 @@ def aggregate_datasets( meta_idx = aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx) + # Clear the src_to_dst mapping after processing each source dataset + # to avoid interference between different source datasets + data_idx.pop("src_to_dst", None) + dst_meta.info["total_episodes"] += src_meta.total_episodes dst_meta.info["total_frames"] += src_meta.total_frames @@ -312,10 +359,6 @@ def aggregate_videos(src_meta, dst_meta, videos_idx, video_files_size_in_mb, chu dst_file_durations = video_idx["dst_file_durations"] for src_chunk_idx, src_file_idx in unique_chunk_file_pairs: - # Convert to Python int to ensure consistent dict keys - src_chunk_idx = int(src_chunk_idx) - src_file_idx = int(src_file_idx) - src_path = src_meta.root / DEFAULT_VIDEO_PATH.format( video_key=key, chunk_index=src_chunk_idx, @@ -388,10 +431,16 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si Reads source data files, updates indices to match the aggregated dataset, and writes them to the destination with proper file rotation. + Tracks a `src_to_dst` mapping from source (chunk, file) to destination (chunk, file) + which is critical for correctly updating episode metadata when source datasets + have multiple data files (e.g., from a previous merge operation). + Args: src_meta: Source dataset metadata. dst_meta: Destination dataset metadata. data_idx: Dictionary tracking data chunk and file indices. + data_files_size_in_mb: Maximum size for data files in MB. + chunk_size: Maximum number of files per chunk. Returns: dict: Updated data_idx with current chunk and file indices. @@ -409,6 +458,10 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si # retrieve features schema for proper image typing in parquet hf_features = get_hf_features_from_features(dst_meta.features) if contains_images else None + # Track source to destination file mapping for metadata update + # This is critical for handling datasets that are already results of a merge + src_to_dst: dict[tuple[int, int], tuple[int, int]] = {} + for src_chunk_idx, src_file_idx in unique_chunk_file_ids: src_path = src_meta.root / DEFAULT_DATA_PATH.format( chunk_index=src_chunk_idx, file_index=src_file_idx @@ -421,7 +474,9 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si df = pd.read_parquet(src_path) df = update_data_df(df, src_meta, dst_meta) - data_idx = append_or_create_parquet_file( + # Write data and get the actual destination file it was written to + # This avoids duplicating the rotation logic here + data_idx, (dst_chunk, dst_file) = append_or_create_parquet_file( df, src_path, data_idx, @@ -433,6 +488,12 @@ def aggregate_data(src_meta, dst_meta, data_idx, data_files_size_in_mb, chunk_si hf_features=hf_features, ) + # Record the mapping from source to actual destination + src_to_dst[(src_chunk_idx, src_file_idx)] = (dst_chunk, dst_file) + + # Add the mapping to data_idx for use in metadata update + data_idx["src_to_dst"] = src_to_dst + return data_idx @@ -473,7 +534,7 @@ def aggregate_metadata(src_meta, dst_meta, meta_idx, data_idx, videos_idx): videos_idx, ) - meta_idx = append_or_create_parquet_file( + meta_idx, _ = append_or_create_parquet_file( df, src_path, meta_idx, @@ -501,7 +562,7 @@ def append_or_create_parquet_file( contains_images: bool = False, aggr_root: Path = None, hf_features: datasets.Features | None = None, -): +) -> tuple[dict[str, int], tuple[int, int]]: """Appends data to an existing parquet file or creates a new one based on size constraints. Manages file rotation when size limits are exceeded to prevent individual files @@ -519,9 +580,11 @@ def append_or_create_parquet_file( hf_features: Optional HuggingFace Features schema for proper image typing. Returns: - dict: Updated index dictionary with current chunk and file indices. + tuple: (updated_idx, (dst_chunk, dst_file)) where updated_idx is the index dict + and (dst_chunk, dst_file) is the actual destination file the data was written to. """ - dst_path = aggr_root / default_path.format(chunk_index=idx["chunk"], file_index=idx["file"]) + dst_chunk, dst_file = idx["chunk"], idx["file"] + dst_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file) if not dst_path.exists(): dst_path.parent.mkdir(parents=True, exist_ok=True) @@ -529,14 +592,15 @@ def append_or_create_parquet_file( to_parquet_with_hf_images(df, dst_path, features=hf_features) else: df.to_parquet(dst_path) - return idx + return idx, (dst_chunk, dst_file) src_size = get_parquet_file_size_in_mb(src_path) dst_size = get_parquet_file_size_in_mb(dst_path) if dst_size + src_size >= max_mb: idx["chunk"], idx["file"] = update_chunk_file_indices(idx["chunk"], idx["file"], chunk_size) - new_path = aggr_root / default_path.format(chunk_index=idx["chunk"], file_index=idx["file"]) + dst_chunk, dst_file = idx["chunk"], idx["file"] + new_path = aggr_root / default_path.format(chunk_index=dst_chunk, file_index=dst_file) new_path.parent.mkdir(parents=True, exist_ok=True) final_df = df target_path = new_path @@ -555,7 +619,7 @@ def append_or_create_parquet_file( else: final_df.to_parquet(target_path) - return idx + return idx, (dst_chunk, dst_file) def finalize_aggregation(aggr_meta, all_metadata): diff --git a/src/lerobot/datasets/dataset_tools.py b/src/lerobot/datasets/dataset_tools.py index e2928e2a6..123d455c6 100644 --- a/src/lerobot/datasets/dataset_tools.py +++ b/src/lerobot/datasets/dataset_tools.py @@ -1396,6 +1396,132 @@ BYTES_PER_KIB = 1024 BYTES_PER_MIB = BYTES_PER_KIB * BYTES_PER_KIB +def modify_tasks( + dataset: LeRobotDataset, + new_task: str | None = None, + episode_tasks: dict[int, str] | None = None, +) -> LeRobotDataset: + """Modify tasks in a LeRobotDataset. + + This function allows you to either: + 1. Set a single task for the entire dataset (using `new_task`) + 2. Set specific tasks for specific episodes (using `episode_tasks`) + + You can combine both: `new_task` sets the default, and `episode_tasks` overrides + specific episodes. + + The dataset is modified in-place, updating only the task-related files: + - meta/tasks.parquet + - data/**/*.parquet (task_index column) + - meta/episodes/**/*.parquet (tasks column) + - meta/info.json (total_tasks) + + Args: + dataset: The source LeRobotDataset to modify. + new_task: A single task string to apply to all episodes. If None and episode_tasks + is also None, raises an error. + episode_tasks: Optional dict mapping episode indices to their task strings. + Overrides `new_task` for specific episodes. + + + Examples: + Set a single task for all episodes: + dataset = modify_tasks(dataset, new_task="Pick up the cube") + + Set different tasks for specific episodes: + dataset = modify_tasks( + dataset, + episode_tasks={0: "Task A", 1: "Task B", 2: "Task A"} + ) + + Set a default task with overrides: + dataset = modify_tasks( + dataset, + new_task="Default task", + episode_tasks={5: "Special task for episode 5"} + ) + """ + if new_task is None and episode_tasks is None: + raise ValueError("Must specify at least one of new_task or episode_tasks") + + if episode_tasks is not None: + valid_indices = set(range(dataset.meta.total_episodes)) + invalid = set(episode_tasks.keys()) - valid_indices + if invalid: + raise ValueError(f"Invalid episode indices: {invalid}") + + # Ensure episodes metadata is loaded + if dataset.meta.episodes is None: + dataset.meta.episodes = load_episodes(dataset.root) + + # Build the mapping from episode index to task string + episode_to_task: dict[int, str] = {} + for ep_idx in range(dataset.meta.total_episodes): + if episode_tasks and ep_idx in episode_tasks: + episode_to_task[ep_idx] = episode_tasks[ep_idx] + elif new_task is not None: + episode_to_task[ep_idx] = new_task + else: + # Keep original task if not overridden and no default provided + original_tasks = dataset.meta.episodes[ep_idx]["tasks"] + if not original_tasks: + raise ValueError(f"Episode {ep_idx} has no tasks and no default task was provided") + episode_to_task[ep_idx] = original_tasks[0] + + # Collect all unique tasks and create new task mapping + unique_tasks = sorted(set(episode_to_task.values())) + new_task_df = pd.DataFrame({"task_index": list(range(len(unique_tasks)))}, index=unique_tasks) + task_to_index = {task: idx for idx, task in enumerate(unique_tasks)} + + logging.info(f"Modifying tasks in {dataset.repo_id}") + logging.info(f"New tasks: {unique_tasks}") + + root = dataset.root + + # Update data files - modify task_index column + logging.info("Updating data files...") + data_dir = root / DATA_DIR + + for parquet_path in tqdm(sorted(data_dir.rglob("*.parquet")), desc="Updating data"): + df = pd.read_parquet(parquet_path) + + # Build a mapping from episode_index to new task_index for rows in this file + episode_indices_in_file = df["episode_index"].unique() + ep_to_new_task_idx = { + ep_idx: task_to_index[episode_to_task[ep_idx]] for ep_idx in episode_indices_in_file + } + + # Update task_index column + df["task_index"] = df["episode_index"].map(ep_to_new_task_idx) + df.to_parquet(parquet_path, index=False) + + # Update episodes metadata - modify tasks column + logging.info("Updating episodes metadata...") + episodes_dir = root / "meta" / "episodes" + + for parquet_path in tqdm(sorted(episodes_dir.rglob("*.parquet")), desc="Updating episodes"): + df = pd.read_parquet(parquet_path) + + # Update tasks column + df["tasks"] = df["episode_index"].apply(lambda ep_idx: [episode_to_task[ep_idx]]) + df.to_parquet(parquet_path, index=False) + + # Write new tasks.parquet + write_tasks(new_task_df, root) + + # Update info.json + dataset.meta.info["total_tasks"] = len(unique_tasks) + write_info(dataset.meta.info, root) + + # Reload metadata to reflect changes + dataset.meta.tasks = new_task_df + dataset.meta.episodes = load_episodes(root) + + logging.info(f"Tasks: {unique_tasks}") + + return dataset + + def convert_image_to_video_dataset( dataset: LeRobotDataset, output_dir: Path, diff --git a/src/lerobot/datasets/lerobot_dataset.py b/src/lerobot/datasets/lerobot_dataset.py index f59db7a43..7df2fe7e2 100644 --- a/src/lerobot/datasets/lerobot_dataset.py +++ b/src/lerobot/datasets/lerobot_dataset.py @@ -1098,11 +1098,13 @@ class LeRobotDataset(torch.utils.data.Dataset): item["robot_utterance"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["robot_utterance"] item["user_prompt"] = self.meta.tasks_high_level.iloc[high_level_task_idx]["user_prompt"] - # optionally add subtask information + + + # add subtask information if available if "subtask_index" in self.features and self.meta.subtasks is not None: subtask_idx = item["subtask_index"].item() item["subtask"] = self.meta.subtasks.iloc[subtask_idx].name - + return item def __repr__(self): diff --git a/src/lerobot/datasets/transforms.py b/src/lerobot/datasets/transforms.py index beacc48d9..5240619cb 100644 --- a/src/lerobot/datasets/transforms.py +++ b/src/lerobot/datasets/transforms.py @@ -216,16 +216,17 @@ class ImageTransformsConfig: def make_transform_from_config(cfg: ImageTransformConfig): - if cfg.type == "Identity": - return v2.Identity(**cfg.kwargs) - elif cfg.type == "ColorJitter": - return v2.ColorJitter(**cfg.kwargs) - elif cfg.type == "SharpnessJitter": + if cfg.type == "SharpnessJitter": return SharpnessJitter(**cfg.kwargs) - elif cfg.type == "RandomAffine": - return v2.RandomAffine(**cfg.kwargs) - else: - raise ValueError(f"Transform '{cfg.type}' is not valid.") + + transform_cls = getattr(v2, cfg.type, None) + if isinstance(transform_cls, type) and issubclass(transform_cls, Transform): + return transform_cls(**cfg.kwargs) + + raise ValueError( + f"Transform '{cfg.type}' is not valid. It must be a class in " + f"torchvision.transforms.v2 or 'SharpnessJitter'." + ) class ImageTransforms(Transform): diff --git a/src/lerobot/datasets/utils.py b/src/lerobot/datasets/utils.py index 013fb34df..e97a82b49 100644 --- a/src/lerobot/datasets/utils.py +++ b/src/lerobot/datasets/utils.py @@ -60,6 +60,7 @@ VIDEO_DIR = "videos" CHUNK_FILE_PATTERN = "chunk-{chunk_index:03d}/file-{file_index:03d}" DEFAULT_TASKS_PATH = "meta/tasks.parquet" +DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet" DEFAULT_EPISODES_PATH = EPISODES_DIR + "/" + CHUNK_FILE_PATTERN + ".parquet" DEFAULT_TASKS_HIGH_LEVEL_PATH = "meta/tasks_high_level.parquet" DEFAULT_SUBTASKS_PATH = "meta/subtasks.parquet" @@ -369,6 +370,14 @@ def load_subtasks(local_dir: Path) -> pandas.DataFrame | None: return pd.read_parquet(subtasks_path) return None +def load_subtasks(local_dir: Path) -> pandas.DataFrame | None: + """Load subtasks from subtasks.parquet if it exists.""" + subtasks_path = local_dir / DEFAULT_SUBTASKS_PATH + if subtasks_path.exists(): + return pd.read_parquet(subtasks_path) + return None + + def write_episodes(episodes: Dataset, local_dir: Path) -> None: """Write episode metadata to a parquet file in the LeRobot v3.0 format. This function writes episode-level metadata to a single parquet file. diff --git a/src/lerobot/envs/configs.py b/src/lerobot/envs/configs.py index cd88b37bc..9c1c083a4 100644 --- a/src/lerobot/envs/configs.py +++ b/src/lerobot/envs/configs.py @@ -205,6 +205,7 @@ class ObservationConfig: add_joint_velocity_to_observation: bool = False add_current_to_observation: bool = False + add_ee_pose_to_observation: bool = False display_cameras: bool = False diff --git a/src/lerobot/motors/damiao/damiao.py b/src/lerobot/motors/damiao/damiao.py index dd0213fc3..c79f8d17e 100644 --- a/src/lerobot/motors/damiao/damiao.py +++ b/src/lerobot/motors/damiao/damiao.py @@ -28,8 +28,11 @@ from lerobot.utils.import_utils import _can_available if TYPE_CHECKING or _can_available: import can else: - can.Message = object - can.interface = None + + class can: # noqa: N801 + Message = object + interface = None + import numpy as np @@ -206,11 +209,31 @@ class DamiaoMotorsBus(MotorsBusBase): Raises ConnectionError if any motor fails to respond. """ logger.info("Starting handshake with motors...") - missing_motors = [] + # Drain any pending messages + while self.canbus.recv(timeout=0.01): + pass + + missing_motors = [] for motor_name in self.motors: - msg = self._refresh_motor(motor_name) - if msg is None: + motor_id = self._get_motor_id(motor_name) + recv_id = self._get_motor_recv_id(motor_name) + + # Send enable command + data = [0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, 0xFF, CAN_CMD_ENABLE] + msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd) + self.canbus.send(msg) + + # Wait for response with longer timeout + response = None + start_time = time.time() + while time.time() - start_time < 0.1: + response = self.canbus.recv(timeout=0.1) + if response and response.arbitration_id == recv_id: + break + response = None + + if response is None: missing_motors.append(motor_name) else: self._process_response(motor_name, msg) @@ -259,7 +282,7 @@ class DamiaoMotorsBus(MotorsBusBase): motor_name = self._get_motor_name(motor) recv_id = self._get_motor_recv_id(motor) data = [0xFF] * 7 + [command_byte] - msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False) + msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd) self.canbus.send(msg) if msg := self._recv_motor_response(expected_recv_id=recv_id): self._process_response(motor_name, msg) @@ -317,7 +340,7 @@ class DamiaoMotorsBus(MotorsBusBase): motor_id = self._get_motor_id(motor) recv_id = self._get_motor_recv_id(motor) data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0] - msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False) + msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False, is_fd=self.use_can_fd) self.canbus.send(msg) return self._recv_motor_response(expected_recv_id=recv_id) @@ -439,7 +462,7 @@ class DamiaoMotorsBus(MotorsBusBase): motor_type = self._motor_types[motor_name] data = self._encode_mit_packet(motor_type, kp, kd, position_degrees, velocity_deg_per_sec, torque) - msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False) + msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd) self.canbus.send(msg) recv_id = self._get_motor_recv_id(motor) @@ -472,7 +495,7 @@ class DamiaoMotorsBus(MotorsBusBase): motor_type = self._motor_types[motor_name] data = self._encode_mit_packet(motor_type, kp, kd, position_degrees, velocity_deg_per_sec, torque) - msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False) + msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd) self.canbus.send(msg) recv_id_to_motor[self._get_motor_recv_id(motor)] = motor_name @@ -637,10 +660,10 @@ class DamiaoMotorsBus(MotorsBusBase): for motor in motors: motor_id = self._get_motor_id(motor) data = [motor_id & 0xFF, (motor_id >> 8) & 0xFF, CAN_CMD_REFRESH, 0, 0, 0, 0, 0] - msg = can.Message(arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False) + msg = can.Message( + arbitration_id=CAN_PARAM_ID, data=data, is_extended_id=False, is_fd=self.use_can_fd + ) self.canbus.send(msg) - # Small delay to reduce bus congestion if necessary, though removed in sync_read previously - # precise_sleep(PRECISE_SLEEP_SEC) # Collect responses expected_recv_ids = [self._get_motor_recv_id(m) for m in motors] @@ -676,7 +699,9 @@ class DamiaoMotorsBus(MotorsBusBase): kd = self._gains[motor]["kd"] data = self._encode_mit_packet(motor_type, kp, kd, float(value_degrees), 0.0, 0.0) - msg = can.Message(arbitration_id=motor_id, data=data, is_extended_id=False) + msg = can.Message( + arbitration_id=motor_id, data=data, is_extended_id=False, is_fd=self.use_can_fd + ) self.canbus.send(msg) precise_sleep(PRECISE_TIMEOUT_SEC) diff --git a/src/lerobot/policies/act/configuration_act.py b/src/lerobot/policies/act/configuration_act.py index 6f6c1c4be..bd89185fd 100644 --- a/src/lerobot/policies/act/configuration_act.py +++ b/src/lerobot/policies/act/configuration_act.py @@ -28,7 +28,7 @@ class ACTConfig(PreTrainedConfig): Defaults are configured for training on bimanual Aloha tasks like "insertion" or "transfer". The parameters you will most likely need to change are the ones which depend on the environment / sensors. - Those are: `input_shapes` and 'output_shapes`. + Those are: `input_features` and `output_features`. Notes on the inputs and outputs: - Either: @@ -48,21 +48,12 @@ class ACTConfig(PreTrainedConfig): This should be no greater than the chunk size. For example, if the chunk size size 100, you may set this to 50. This would mean that the model predicts 100 steps worth of actions, runs 50 in the environment, and throws the other 50 out. - input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents - the input data name, and the value is a list indicating the dimensions of the corresponding data. - For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96], - indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't - include batch dimension or temporal dimension. - output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents - the output data name, and the value is a list indicating the dimensions of the corresponding data. - For example, "action" refers to an output shape of [14], indicating 14-dimensional actions. - Importantly, `output_shapes` doesn't include batch dimension or temporal dimension. - input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), - and the value specifies the normalization mode to apply. The two available modes are "mean_std" - which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a - [-1, 1] range. - output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the - original scale. Note that this is also used for normalizing the training targets. + input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents + the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents + the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to + a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX) vision_backbone: Name of the torchvision resnet backbone to use for encoding images. pretrained_backbone_weights: Pretrained weights from torchvision to initialize the backbone. `None` means no pretrained weights. diff --git a/src/lerobot/policies/diffusion/configuration_diffusion.py b/src/lerobot/policies/diffusion/configuration_diffusion.py index 54569434a..8ac0920dd 100644 --- a/src/lerobot/policies/diffusion/configuration_diffusion.py +++ b/src/lerobot/policies/diffusion/configuration_diffusion.py @@ -30,7 +30,7 @@ class DiffusionConfig(PreTrainedConfig): Defaults are configured for training with PushT providing proprioceptive and single camera observations. The parameters you will most likely need to change are the ones which depend on the environment / sensors. - Those are: `input_shapes` and `output_shapes`. + Those are: `input_features` and `output_features`. Notes on the inputs and outputs: - "observation.state" is required as an input key. @@ -48,21 +48,12 @@ class DiffusionConfig(PreTrainedConfig): horizon: Diffusion model action prediction size as detailed in `DiffusionPolicy.select_action`. n_action_steps: The number of action steps to run in the environment for one invocation of the policy. See `DiffusionPolicy.select_action` for more details. - input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents - the input data name, and the value is a list indicating the dimensions of the corresponding data. - For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96], - indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't - include batch dimension or temporal dimension. - output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents - the output data name, and the value is a list indicating the dimensions of the corresponding data. - For example, "action" refers to an output shape of [14], indicating 14-dimensional actions. - Importantly, `output_shapes` doesn't include batch dimension or temporal dimension. - input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), - and the value specifies the normalization mode to apply. The two available modes are "mean_std" - which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a - [-1, 1] range. - output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the - original scale. Note that this is also used for normalizing the training targets. + input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents + the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents + the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to + a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX) vision_backbone: Name of the torchvision resnet backbone to use for encoding images. crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit within the image size. If None, no cropping is done. @@ -73,7 +64,7 @@ class DiffusionConfig(PreTrainedConfig): use_group_norm: Whether to replace batch normalization with group normalization in the backbone. The group sizes are set to be about 16 (to be precise, feature_dim // 16). spatial_softmax_num_keypoints: Number of keypoints for SpatialSoftmax. - use_separate_rgb_encoders_per_camera: Whether to use a separate RGB encoder for each camera view. + use_separate_rgb_encoder_per_camera: Whether to use a separate RGB encoder for each camera view. down_dims: Feature dimension for each stage of temporal downsampling in the diffusion modeling Unet. You may provide a variable number of dimensions, therefore also controlling the degree of downsampling. diff --git a/src/lerobot/policies/sac/modeling_sac.py b/src/lerobot/policies/sac/modeling_sac.py index c7c6798ed..d5dd71a48 100644 --- a/src/lerobot/policies/sac/modeling_sac.py +++ b/src/lerobot/policies/sac/modeling_sac.py @@ -239,8 +239,10 @@ class SACPolicy( + target_param.data * (1.0 - self.config.critic_target_update_weight) ) - def update_temperature(self): - self.temperature = self.log_alpha.exp().item() + @property + def temperature(self) -> float: + """Return the current temperature value, always in sync with log_alpha.""" + return self.log_alpha.exp().item() def compute_loss_critic( self, @@ -457,11 +459,10 @@ class SACPolicy( dim = continuous_action_dim + (1 if self.config.num_discrete_actions is not None else 0) self.target_entropy = -np.prod(dim) / 2 - def _init_temperature(self): - """Set up temperature parameter and initial log_alpha.""" + def _init_temperature(self) -> None: + """Set up temperature parameter (log_alpha).""" temp_init = self.config.temperature_init self.log_alpha = nn.Parameter(torch.tensor([math.log(temp_init)])) - self.temperature = self.log_alpha.exp().item() class SACObservationEncoder(nn.Module): diff --git a/src/lerobot/policies/tdmpc/configuration_tdmpc.py b/src/lerobot/policies/tdmpc/configuration_tdmpc.py index 3c1a29932..3ec493472 100644 --- a/src/lerobot/policies/tdmpc/configuration_tdmpc.py +++ b/src/lerobot/policies/tdmpc/configuration_tdmpc.py @@ -30,7 +30,7 @@ class TDMPCConfig(PreTrainedConfig): camera observations. The parameters you will most likely need to change are the ones which depend on the environment / sensors. - Those are: `input_shapes`, `output_shapes`, and perhaps `max_random_shift_ratio`. + Those are: `input_features`, `output_features`, and perhaps `max_random_shift_ratio`. Args: n_action_repeats: The number of times to repeat the action returned by the planning. (hint: Google @@ -40,24 +40,12 @@ class TDMPCConfig(PreTrainedConfig): is an alternative to using action repeats. If this is set to more than 1, then we require `n_action_repeats == 1`, `use_mpc == True` and `n_action_steps <= horizon`. Note that this approach of using multiple steps from the plan is not in the original implementation. - input_shapes: A dictionary defining the shapes of the input data for the policy. The key represents - the input data name, and the value is a list indicating the dimensions of the corresponding data. - For example, "observation.image" refers to an input from a camera with dimensions [3, 96, 96], - indicating it has three color channels and 96x96 resolution. Importantly, `input_shapes` doesn't - include batch dimension or temporal dimension. - output_shapes: A dictionary defining the shapes of the output data for the policy. The key represents - the output data name, and the value is a list indicating the dimensions of the corresponding data. - For example, "action" refers to an output shape of [14], indicating 14-dimensional actions. - Importantly, `output_shapes` doesn't include batch dimension or temporal dimension. - input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), - and the value specifies the normalization mode to apply. The two available modes are "mean_std" - which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a - [-1, 1] range. Note that here this defaults to None meaning inputs are not normalized. This is to - match the original implementation. - output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the - original scale. Note that this is also used for normalizing the training targets. NOTE: Clipping - to [-1, +1] is used during MPPI/CEM. Therefore, it is recommended that you stick with "min_max" - normalization mode here. + input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents + the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents + the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to + a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX) image_encoder_hidden_dim: Number of channels for the convolutional layers used for image encoding. state_encoder_hidden_dim: Hidden dimension for MLP used for state vector encoding. latent_dim: Observation's latent embedding dimension. diff --git a/src/lerobot/policies/vqbet/configuration_vqbet.py b/src/lerobot/policies/vqbet/configuration_vqbet.py index 44ada9f17..32906e528 100644 --- a/src/lerobot/policies/vqbet/configuration_vqbet.py +++ b/src/lerobot/policies/vqbet/configuration_vqbet.py @@ -32,7 +32,7 @@ class VQBeTConfig(PreTrainedConfig): Defaults are configured for training with PushT providing proprioceptive and single camera observations. The parameters you will most likely need to change are the ones which depend on the environment / sensors. - Those are: `input_shapes` and `output_shapes`. + Those are: `input_features` and `output_features`. Notes on the inputs and outputs: - "observation.state" is required as an input key. @@ -46,21 +46,12 @@ class VQBeTConfig(PreTrainedConfig): current step and additional steps going back). n_action_pred_token: Total number of current token and future tokens that VQ-BeT predicts. action_chunk_size: Action chunk size of each action prediction token. - input_shapes: A dictionary defining the shapes of the input data for the policy. - The key represents the input data name, and the value is a list indicating the dimensions - of the corresponding data. For example, "observation.image" refers to an input from - a camera with dimensions [3, 96, 96], indicating it has three color channels and 96x96 resolution. - Importantly, shapes doesnt include batch dimension or temporal dimension. - output_shapes: A dictionary defining the shapes of the output data for the policy. - The key represents the output data name, and the value is a list indicating the dimensions - of the corresponding data. For example, "action" refers to an output shape of [14], indicating - 14-dimensional actions. Importantly, shapes doesnt include batch dimension or temporal dimension. - input_normalization_modes: A dictionary with key representing the modality (e.g. "observation.state"), - and the value specifies the normalization mode to apply. The two available modes are "mean_std" - which subtracts the mean and divides by the standard deviation and "min_max" which rescale in a - [-1, 1] range. - output_normalization_modes: Similar dictionary as `normalize_input_modes`, but to unnormalize to the - original scale. Note that this is also used for normalizing the training targets. + input_features: A dictionary defining the PolicyFeature of the input data for the policy. The key represents + the input data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + output_features: A dictionary defining the PolicyFeature of the output data for the policy. The key represents + the output data name, and the value is PolicyFeature, which consists of FeatureType and shape attributes. + normalization_mapping: A dictionary that maps from a str value of FeatureType (e.g., "STATE", "VISUAL") to + a corresponding NormalizationMode (e.g., NormalizationMode.MIN_MAX) vision_backbone: Name of the torchvision resnet backbone to use for encoding images. crop_shape: (H, W) shape to crop images to as a preprocessing step for the vision backbone. Must fit within the image size. If None, no cropping is done. diff --git a/src/lerobot/processor/converters.py b/src/lerobot/processor/converters.py index fd4833c28..ef05dee2c 100644 --- a/src/lerobot/processor/converters.py +++ b/src/lerobot/processor/converters.py @@ -168,6 +168,7 @@ def _extract_complementary_data(batch: dict[str, Any]) -> dict[str, Any]: """ pad_keys = {k: v for k, v in batch.items() if "_is_pad" in k} task_key = {"task": batch["task"]} if "task" in batch else {} + subtask_key = {"subtask": batch["subtask"]} if "subtask" in batch else {} index_key = {"index": batch["index"]} if "index" in batch else {} task_index_key = {"task_index": batch["task_index"]} if "task_index" in batch else {} user_prompt_key = {"user_prompt": batch["user_prompt"]} if "user_prompt" in batch else {} diff --git a/src/lerobot/processor/hil_processor.py b/src/lerobot/processor/hil_processor.py index f0dbac9c3..24b5628fa 100644 --- a/src/lerobot/processor/hil_processor.py +++ b/src/lerobot/processor/hil_processor.py @@ -18,16 +18,18 @@ import math import time from dataclasses import dataclass -from typing import Any, Protocol, TypeVar, runtime_checkable +from typing import TYPE_CHECKING, Any, Protocol, TypeVar, runtime_checkable import numpy as np import torch import torchvision.transforms.functional as F # noqa: N812 from lerobot.configs.types import PipelineFeatureType, PolicyFeature -from lerobot.teleoperators.teleoperator import Teleoperator from lerobot.teleoperators.utils import TeleopEvents +if TYPE_CHECKING: + from lerobot.teleoperators.teleoperator import Teleoperator + from .core import EnvTransition, PolicyAction, TransitionKey from .pipeline import ( ComplementaryDataProcessorStep, @@ -69,10 +71,10 @@ class HasTeleopEvents(Protocol): # Type variable constrained to Teleoperator subclasses that also implement events -TeleopWithEvents = TypeVar("TeleopWithEvents", bound=Teleoperator) +TeleopWithEvents = TypeVar("TeleopWithEvents", bound="Teleoperator") -def _check_teleop_with_events(teleop: Teleoperator) -> None: +def _check_teleop_with_events(teleop: "Teleoperator") -> None: """ Runtime check that a teleoperator implements the `HasTeleopEvents` protocol. @@ -103,7 +105,7 @@ class AddTeleopActionAsComplimentaryDataStep(ComplementaryDataProcessorStep): teleop_device: The teleoperator instance to get the action from. """ - teleop_device: Teleoperator + teleop_device: "Teleoperator" def complementary_data(self, complementary_data: dict) -> dict: """ @@ -312,7 +314,7 @@ class TimeLimitProcessorStep(TruncatedProcessorStep): @dataclass @ProcessorStepRegistry.register("gripper_penalty_processor") -class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep): +class GripperPenaltyProcessorStep(ProcessorStep): """ Applies a penalty for inefficient gripper usage. @@ -327,26 +329,27 @@ class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep): penalty: float = -0.01 max_gripper_pos: float = 30.0 - def complementary_data(self, complementary_data: dict) -> dict: + def __call__(self, transition: EnvTransition) -> EnvTransition: """ Calculates the gripper penalty and adds it to the complementary data. Args: - complementary_data: The incoming complementary data, which should contain - raw joint positions. + transition: The incoming environment transition. Returns: - A new complementary data dictionary with the `discrete_penalty` key added. + The modified transition with the penalty added to complementary data. """ - action = self.transition.get(TransitionKey.ACTION) + new_transition = transition.copy() + action = new_transition.get(TransitionKey.ACTION) + complementary_data = new_transition.get(TransitionKey.COMPLEMENTARY_DATA, {}) raw_joint_positions = complementary_data.get("raw_joint_positions") if raw_joint_positions is None: - return complementary_data + return new_transition current_gripper_pos = raw_joint_positions.get(GRIPPER_KEY, None) if current_gripper_pos is None: - return complementary_data + return new_transition # Gripper action is a PolicyAction at this stage gripper_action = action[-1].item() @@ -362,11 +365,12 @@ class GripperPenaltyProcessorStep(ComplementaryDataProcessorStep): gripper_penalty = self.penalty * int(gripper_penalty_bool) - # Create new complementary data with penalty info + # Update complementary data with penalty info new_complementary_data = dict(complementary_data) new_complementary_data[DISCRETE_PENALTY_KEY] = gripper_penalty + new_transition[TransitionKey.COMPLEMENTARY_DATA] = new_complementary_data - return new_complementary_data + return new_transition def get_config(self) -> dict[str, Any]: """ diff --git a/src/lerobot/processor/tokenizer_processor.py b/src/lerobot/processor/tokenizer_processor.py index c278f5c0e..3e298650c 100644 --- a/src/lerobot/processor/tokenizer_processor.py +++ b/src/lerobot/processor/tokenizer_processor.py @@ -169,7 +169,7 @@ class TokenizerProcessorStep(ObservationProcessorStep): return user_prompt return None - + def get_subtask(self, transition: EnvTransition) -> list[str] | None: """ Extracts the subtask from the transition's complementary data. @@ -281,7 +281,9 @@ class TokenizerProcessorStep(ObservationProcessorStep): # Add tokenized subtask to the observation new_observation[OBS_LANGUAGE_SUBTASK_TOKENS] = tokenized_subtask["input_ids"] - new_observation[OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] = tokenized_subtask["attention_mask"].to(dtype=torch.bool) + new_observation[OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] = tokenized_subtask["attention_mask"].to( + dtype=torch.bool + ) return new_observation diff --git a/src/lerobot/rl/gym_manipulator.py b/src/lerobot/rl/gym_manipulator.py index 3d58ae18f..1c1cb752f 100644 --- a/src/lerobot/rl/gym_manipulator.py +++ b/src/lerobot/rl/gym_manipulator.py @@ -412,7 +412,10 @@ def make_processors( if cfg.processor.observation.add_current_to_observation: env_pipeline_steps.append(MotorCurrentProcessorStep(robot=env.robot)) - if kinematics_solver is not None: + add_ee_pose = ( + cfg.processor.observation is not None and cfg.processor.observation.add_ee_pose_to_observation + ) + if kinematics_solver is not None and add_ee_pose: env_pipeline_steps.append( ForwardKinematicsJointsToEEObservation( kinematics=kinematics_solver, @@ -435,7 +438,12 @@ def make_processors( ) # Add gripper penalty processor if gripper config exists and enabled - if cfg.processor.gripper is not None and cfg.processor.gripper.use_gripper: + # Only add if max_gripper_pos is explicitly configured (required for normalization) + if ( + cfg.processor.gripper is not None + and cfg.processor.gripper.use_gripper + and cfg.processor.max_gripper_pos is not None + ): env_pipeline_steps.append( GripperPenaltyProcessorStep( penalty=cfg.processor.gripper.gripper_penalty, diff --git a/src/lerobot/rl/learner.py b/src/lerobot/rl/learner.py index abc5c9504..ee09ac9ac 100644 --- a/src/lerobot/rl/learner.py +++ b/src/lerobot/rl/learner.py @@ -545,9 +545,6 @@ def add_actor_information_and_train( training_infos["temperature_grad_norm"] = temp_grad_norm training_infos["temperature"] = policy.temperature - # Update temperature - policy.update_temperature() - # Push policy to actors if needed if time.time() - last_time_policy_pushed > policy_parameters_push_frequency: push_actor_policy_to_queue(parameters_queue=parameters_queue, policy=policy) diff --git a/src/lerobot/rl/wandb_utils.py b/src/lerobot/rl/wandb_utils.py index 7b7f8a57b..ee30b75df 100644 --- a/src/lerobot/rl/wandb_utils.py +++ b/src/lerobot/rl/wandb_utils.py @@ -26,8 +26,21 @@ from lerobot.configs.train import TrainPipelineConfig from lerobot.utils.constants import PRETRAINED_MODEL_DIR -def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[str] | str: +def cfg_to_group( + cfg: TrainPipelineConfig, return_list: bool = False, truncate_tags: bool = False, max_tag_length: int = 64 +) -> list[str] | str: """Return a group name for logging. Optionally returns group name as list.""" + + def _maybe_truncate(tag: str) -> str: + """Truncate tag to max_tag_length characters if required. + + wandb rejects tags longer than 64 characters. + See: https://github.com/wandb/wandb/blob/main/wandb/sdk/wandb_settings.py + """ + if len(tag) <= max_tag_length: + return tag + return tag[:max_tag_length] + lst = [ f"policy:{cfg.policy.type}", f"seed:{cfg.seed}", @@ -36,6 +49,8 @@ def cfg_to_group(cfg: TrainPipelineConfig, return_list: bool = False) -> list[st lst.append(f"dataset:{cfg.dataset.repo_id}") if cfg.env is not None: lst.append(f"env:{cfg.env.type}") + if truncate_tags: + lst = [_maybe_truncate(tag) for tag in lst] return lst if return_list else "-".join(lst) @@ -83,7 +98,7 @@ class WandBLogger: entity=self.cfg.entity, name=self.job_name, notes=self.cfg.notes, - tags=cfg_to_group(cfg, return_list=True), + tags=cfg_to_group(cfg, return_list=True, truncate_tags=True), dir=self.log_dir, config=cfg.to_dict(), # TODO(rcadene): try set to True diff --git a/src/lerobot/robots/bi_openarm_follower/__init__.py b/src/lerobot/robots/bi_openarm_follower/__init__.py new file mode 100644 index 000000000..b1dcce431 --- /dev/null +++ b/src/lerobot/robots/bi_openarm_follower/__init__.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .bi_openarm_follower import BiOpenArmFollower +from .config_bi_openarm_follower import BiOpenArmFollowerConfig + +__all__ = ["BiOpenArmFollower", "BiOpenArmFollowerConfig"] diff --git a/src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py b/src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py new file mode 100644 index 000000000..466eb07e5 --- /dev/null +++ b/src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py @@ -0,0 +1,175 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from functools import cached_property + +from lerobot.processor import RobotAction, RobotObservation +from lerobot.robots.openarm_follower import OpenArmFollower, OpenArmFollowerConfig + +from ..robot import Robot +from .config_bi_openarm_follower import BiOpenArmFollowerConfig + +logger = logging.getLogger(__name__) + + +class BiOpenArmFollower(Robot): + """ + Bimanual OpenArm Follower Arms + """ + + config_class = BiOpenArmFollowerConfig + name = "bi_openarm_follower" + + def __init__(self, config: BiOpenArmFollowerConfig): + super().__init__(config) + self.config = config + + left_arm_config = OpenArmFollowerConfig( + id=f"{config.id}_left" if config.id else None, + calibration_dir=config.calibration_dir, + port=config.left_arm_config.port, + disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect, + max_relative_target=config.left_arm_config.max_relative_target, + cameras=config.left_arm_config.cameras, + side=config.left_arm_config.side, + can_interface=config.left_arm_config.can_interface, + use_can_fd=config.left_arm_config.use_can_fd, + can_bitrate=config.left_arm_config.can_bitrate, + can_data_bitrate=config.left_arm_config.can_data_bitrate, + motor_config=config.left_arm_config.motor_config, + position_kd=config.left_arm_config.position_kd, + position_kp=config.left_arm_config.position_kp, + joint_limits=config.left_arm_config.joint_limits, + ) + + right_arm_config = OpenArmFollowerConfig( + id=f"{config.id}_right" if config.id else None, + calibration_dir=config.calibration_dir, + port=config.right_arm_config.port, + disable_torque_on_disconnect=config.right_arm_config.disable_torque_on_disconnect, + max_relative_target=config.right_arm_config.max_relative_target, + cameras=config.right_arm_config.cameras, + side=config.right_arm_config.side, + can_interface=config.right_arm_config.can_interface, + use_can_fd=config.right_arm_config.use_can_fd, + can_bitrate=config.right_arm_config.can_bitrate, + can_data_bitrate=config.right_arm_config.can_data_bitrate, + motor_config=config.right_arm_config.motor_config, + position_kd=config.right_arm_config.position_kd, + position_kp=config.right_arm_config.position_kp, + joint_limits=config.right_arm_config.joint_limits, + ) + + self.left_arm = OpenArmFollower(left_arm_config) + self.right_arm = OpenArmFollower(right_arm_config) + + # Only for compatibility with other parts of the codebase that expect a `robot.cameras` attribute + self.cameras = {**self.left_arm.cameras, **self.right_arm.cameras} + + @property + def _motors_ft(self) -> dict[str, type]: + left_arm_motors_ft = self.left_arm._motors_ft + right_arm_motors_ft = self.right_arm._motors_ft + + return { + **{f"left_{k}": v for k, v in left_arm_motors_ft.items()}, + **{f"right_{k}": v for k, v in right_arm_motors_ft.items()}, + } + + @property + def _cameras_ft(self) -> dict[str, tuple]: + left_arm_cameras_ft = self.left_arm._cameras_ft + right_arm_cameras_ft = self.right_arm._cameras_ft + + return { + **{f"left_{k}": v for k, v in left_arm_cameras_ft.items()}, + **{f"right_{k}": v for k, v in right_arm_cameras_ft.items()}, + } + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + return {**self._motors_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + return self._motors_ft + + @property + def is_connected(self) -> bool: + return self.left_arm.is_connected and self.right_arm.is_connected + + def connect(self, calibrate: bool = True) -> None: + self.left_arm.connect(calibrate) + self.right_arm.connect(calibrate) + + @property + def is_calibrated(self) -> bool: + return self.left_arm.is_calibrated and self.right_arm.is_calibrated + + def calibrate(self) -> None: + self.left_arm.calibrate() + self.right_arm.calibrate() + + def configure(self) -> None: + self.left_arm.configure() + self.right_arm.configure() + + def setup_motors(self) -> None: + raise NotImplementedError( + "Motor ID configuration is typically done via manufacturer tools for CAN motors." + ) + + def get_observation(self) -> RobotObservation: + obs_dict = {} + + # Add "left_" prefix + left_obs = self.left_arm.get_observation() + obs_dict.update({f"left_{key}": value for key, value in left_obs.items()}) + + # Add "right_" prefix + right_obs = self.right_arm.get_observation() + obs_dict.update({f"right_{key}": value for key, value in right_obs.items()}) + + return obs_dict + + def send_action( + self, + action: RobotAction, + custom_kp: dict[str, float] | None = None, + custom_kd: dict[str, float] | None = None, + ) -> RobotAction: + # Remove "left_" prefix + left_action = { + key.removeprefix("left_"): value for key, value in action.items() if key.startswith("left_") + } + # Remove "right_" prefix + right_action = { + key.removeprefix("right_"): value for key, value in action.items() if key.startswith("right_") + } + + sent_action_left = self.left_arm.send_action(left_action, custom_kp, custom_kd) + sent_action_right = self.right_arm.send_action(right_action, custom_kp, custom_kd) + + # Add prefixes back + prefixed_sent_action_left = {f"left_{key}": value for key, value in sent_action_left.items()} + prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()} + + return {**prefixed_sent_action_left, **prefixed_sent_action_right} + + def disconnect(self): + self.left_arm.disconnect() + self.right_arm.disconnect() diff --git a/src/lerobot/robots/bi_openarm_follower/config_bi_openarm_follower.py b/src/lerobot/robots/bi_openarm_follower/config_bi_openarm_follower.py new file mode 100644 index 000000000..9d11f7b4e --- /dev/null +++ b/src/lerobot/robots/bi_openarm_follower/config_bi_openarm_follower.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from lerobot.robots.openarm_follower import OpenArmFollowerConfigBase + +from ..config import RobotConfig + + +@RobotConfig.register_subclass("bi_openarm_follower") +@dataclass +class BiOpenArmFollowerConfig(RobotConfig): + """Configuration class for Bi OpenArm Follower robots.""" + + left_arm_config: OpenArmFollowerConfigBase + right_arm_config: OpenArmFollowerConfigBase diff --git a/src/lerobot/robots/openarm_follower/__init__.py b/src/lerobot/robots/openarm_follower/__init__.py new file mode 100644 index 000000000..217432fd5 --- /dev/null +++ b/src/lerobot/robots/openarm_follower/__init__.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .config_openarm_follower import OpenArmFollowerConfig, OpenArmFollowerConfigBase +from .openarm_follower import OpenArmFollower + +__all__ = ["OpenArmFollower", "OpenArmFollowerConfig", "OpenArmFollowerConfigBase"] diff --git a/src/lerobot/robots/openarm_follower/config_openarm_follower.py b/src/lerobot/robots/openarm_follower/config_openarm_follower.py new file mode 100644 index 000000000..88d81fd50 --- /dev/null +++ b/src/lerobot/robots/openarm_follower/config_openarm_follower.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from lerobot.cameras import CameraConfig + +from ..config import RobotConfig + +LEFT_DEFAULT_JOINTS_LIMITS: dict[str, tuple[float, float]] = { + "joint_1": (-75.0, 75.0), + "joint_2": (-90.0, 9.0), + "joint_3": (-85.0, 85.0), + "joint_4": (0.0, 135.0), + "joint_5": (-85.0, 85.0), + "joint_6": (-40.0, 40.0), + "joint_7": (-80.0, 80.0), + "gripper": (-65.0, 0.0), +} + +RIGHT_DEFAULT_JOINTS_LIMITS: dict[str, tuple[float, float]] = { + "joint_1": (-75.0, 75.0), + "joint_2": (-9.0, 90.0), + "joint_3": (-85.0, 85.0), + "joint_4": (0.0, 135.0), + "joint_5": (-85.0, 85.0), + "joint_6": (-40.0, 40.0), + "joint_7": (-80.0, 80.0), + "gripper": (-65.0, 0.0), +} + + +@dataclass +class OpenArmFollowerConfigBase: + """Base configuration for the OpenArms follower robot with Damiao motors.""" + + # CAN interfaces - one per arm + # arm CAN interface (e.g., "can1") + # Linux: "can0", "can1", etc. + port: str + + # side of the arm: "left" or "right". If "None" default values will be used + side: str | None = None + + # CAN interface type: "socketcan" (Linux), "slcan" (serial), or "auto" (auto-detect) + can_interface: str = "socketcan" + + # CAN FD settings (OpenArms uses CAN FD by default) + use_can_fd: bool = True + can_bitrate: int = 1000000 # Nominal bitrate (1 Mbps) + can_data_bitrate: int = 5000000 # Data bitrate for CAN FD (5 Mbps) + + # Whether to disable torque when disconnecting + disable_torque_on_disconnect: bool = True + + # Safety limit for relative target positions + # Set to a positive scalar for all motors, or a dict mapping motor names to limits + max_relative_target: float | dict[str, float] | None = None + + # Camera configurations + cameras: dict[str, CameraConfig] = field(default_factory=dict) + + # Motor configuration for OpenArms (7 DOF per arm) + # Maps motor names to (send_can_id, recv_can_id, motor_type) + # Based on: https://docs.openarm.dev/software/setup/configure-test + # OpenArms uses 4 types of motors: + # - DM8009 (DM-J8009P-2EC) for shoulders (high torque) + # - DM4340P and DM4340 for shoulder rotation and elbow + # - DM4310 (DM-J4310-2EC V1.1) for wrist and gripper + motor_config: dict[str, tuple[int, int, str]] = field( + default_factory=lambda: { + "joint_1": (0x01, 0x11, "dm8009"), # J1 - Shoulder pan (DM8009) + "joint_2": (0x02, 0x12, "dm8009"), # J2 - Shoulder lift (DM8009) + "joint_3": (0x03, 0x13, "dm4340"), # J3 - Shoulder rotation (DM4340) + "joint_4": (0x04, 0x14, "dm4340"), # J4 - Elbow flex (DM4340) + "joint_5": (0x05, 0x15, "dm4310"), # J5 - Wrist roll (DM4310) + "joint_6": (0x06, 0x16, "dm4310"), # J6 - Wrist pitch (DM4310) + "joint_7": (0x07, 0x17, "dm4310"), # J7 - Wrist rotation (DM4310) + "gripper": (0x08, 0x18, "dm4310"), # J8 - Gripper (DM4310) + } + ) + + # MIT control parameters for position control (used in send_action) + # List of 8 values: [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7, gripper] + position_kp: list[float] = field( + default_factory=lambda: [240.0, 240.0, 240.0, 240.0, 24.0, 31.0, 25.0, 25.0] + ) + position_kd: list[float] = field(default_factory=lambda: [5.0, 5.0, 3.0, 5.0, 0.3, 0.3, 0.3, 0.3]) + + # Values for joint limits. Can be overridden via CLI (for custom values) or by setting config.side to either 'left' or 'right'. + # If config.side is left set to None and no CLI values are passed, the default joint limit values are small for safety. + joint_limits: dict[str, tuple[float, float]] = field( + default_factory=lambda: { + "joint_1": (-5.0, 5.0), + "joint_2": (-5.0, 5.0), + "joint_3": (-5.0, 5.0), + "joint_4": (0.0, 5.0), + "joint_5": (-5.0, 5.0), + "joint_6": (-5.0, 5.0), + "joint_7": (-5.0, 5.0), + "gripper": (-5.0, 0.0), + } + ) + + +@RobotConfig.register_subclass("openarm_follower") +@dataclass +class OpenArmFollowerConfig(RobotConfig, OpenArmFollowerConfigBase): + pass diff --git a/src/lerobot/robots/openarm_follower/openarm_follower.py b/src/lerobot/robots/openarm_follower/openarm_follower.py new file mode 100644 index 000000000..c221afd10 --- /dev/null +++ b/src/lerobot/robots/openarm_follower/openarm_follower.py @@ -0,0 +1,348 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from functools import cached_property +from typing import Any + +from lerobot.cameras.utils import make_cameras_from_configs +from lerobot.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.motors.damiao import DamiaoMotorsBus +from lerobot.processor import RobotAction, RobotObservation +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..robot import Robot +from ..utils import ensure_safe_goal_position +from .config_openarm_follower import ( + LEFT_DEFAULT_JOINTS_LIMITS, + RIGHT_DEFAULT_JOINTS_LIMITS, + OpenArmFollowerConfig, +) + +logger = logging.getLogger(__name__) + + +class OpenArmFollower(Robot): + """ + OpenArms Follower Robot which uses CAN bus communication to control 7 DOF arm with a gripper. + The arm uses Damiao motors in MIT control mode. + """ + + config_class = OpenArmFollowerConfig + name = "openarm_follower" + + def __init__(self, config: OpenArmFollowerConfig): + super().__init__(config) + self.config = config + + # Arm motors + motors: dict[str, Motor] = {} + for motor_name, (send_id, recv_id, motor_type_str) in config.motor_config.items(): + motor = Motor( + send_id, motor_type_str, MotorNormMode.DEGREES + ) # Always use degrees for Damiao motors + motor.recv_id = recv_id + motor.motor_type_str = motor_type_str + motors[motor_name] = motor + + self.bus = DamiaoMotorsBus( + port=self.config.port, + motors=motors, + calibration=self.calibration, + can_interface=self.config.can_interface, + use_can_fd=self.config.use_can_fd, + bitrate=self.config.can_bitrate, + data_bitrate=self.config.can_data_bitrate if self.config.use_can_fd else None, + ) + + if config.side is not None: + if config.side == "left": + config.joint_limits = LEFT_DEFAULT_JOINTS_LIMITS + elif config.side == "right": + config.joint_limits = RIGHT_DEFAULT_JOINTS_LIMITS + else: + raise ValueError( + "config.side must be either 'left', 'right' (for default values) or 'None' (for CLI values)" + ) + else: + logger.info( + "Set config.side to either 'left' or 'right' to use pre-configured values for joint limits." + ) + logger.info(f"Values used for joint limits: {config.joint_limits}.") + + # Initialize cameras + self.cameras = make_cameras_from_configs(config.cameras) + + @property + def _motors_ft(self) -> dict[str, type]: + """Motor features for observation and action spaces.""" + features: dict[str, type] = {} + for motor in self.bus.motors: + features[f"{motor}.pos"] = float + features[f"{motor}.vel"] = float # Add this + features[f"{motor}.torque"] = float # Add this + return features + + @property + def _cameras_ft(self) -> dict[str, tuple]: + """Camera features for observation space.""" + return { + cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras + } + + @cached_property + def observation_features(self) -> dict[str, type | tuple]: + """Combined observation features from motors and cameras.""" + return {**self._motors_ft, **self._cameras_ft} + + @cached_property + def action_features(self) -> dict[str, type]: + """Action features.""" + return self._motors_ft + + @property + def is_connected(self) -> bool: + """Check if robot is connected.""" + return self.bus.is_connected and all(cam.is_connected for cam in self.cameras.values()) + + def connect(self, calibrate: bool = True) -> None: + """ + Connect to the robot and optionally calibrate. + + We assume that at connection time, the arms are in a safe rest position, + and torque can be safely disabled to run calibration if needed. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + # Connect to CAN bus + logger.info(f"Connecting arm on {self.config.port}...") + self.bus.connect() + + # Run calibration if needed + if not self.is_calibrated and calibrate: + logger.info( + "Mismatch between calibration values in the motor and the calibration file or no calibration file found" + ) + self.calibrate() + + for cam in self.cameras.values(): + cam.connect() + + self.configure() + + if self.is_calibrated: + self.bus.set_zero_position() + + self.bus.enable_torque() + + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + """Check if robot is calibrated.""" + return self.bus.is_calibrated + + def calibrate(self) -> None: + """ + Run calibration procedure for OpenArms robot. + + The calibration procedure: + 1. Disable torque + 2. Ask user to position arms in hanging position with grippers closed + 3. Set this as zero position + 4. Record range of motion for each joint + 5. Save calibration + """ + if self.calibration: + # Calibration file exists, ask user whether to use it or run new calibration + user_input = input( + f"Press ENTER to use provided calibration file associated with the id {self.id}, or type 'c' and press ENTER to run calibration: " + ) + if user_input.strip().lower() != "c": + logger.info(f"Writing calibration file associated with the id {self.id} to the motors") + self.bus.write_calibration(self.calibration) + return + + logger.info(f"\nRunning calibration for {self}") + self.bus.disable_torque() + + # Step 1: Set zero position + input( + "\nCalibration: Set Zero Position)\n" + "Position the arm in the following configuration:\n" + " - Arm hanging straight down\n" + " - Gripper closed\n" + "Press ENTER when ready..." + ) + + # Set current position as zero for all motors + self.bus.set_zero_position() + logger.info("Arm zero position set.") + + logger.info("Setting range: -90° to +90° for safety by default for all joints") + for motor_name, motor in self.bus.motors.items(): + self.calibration[motor_name] = MotorCalibration( + id=motor.id, + drive_mode=0, + homing_offset=0, + range_min=-90, + range_max=90, + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + """Configure motors with appropriate settings.""" + # TODO(Steven, Pepijn): Slightly different from what it is happening in the leader + with self.bus.torque_disabled(): + self.bus.configure_motors() + + def setup_motors(self) -> None: + raise NotImplementedError( + "Motor ID configuration is typically done via manufacturer tools for CAN motors." + ) + + def get_observation(self) -> RobotObservation: + """ + Get current observation from robot including position, velocity, and torque. + + Reads all motor states (pos/vel/torque) in one CAN refresh cycle + instead of 3 separate reads. + """ + start = time.perf_counter() + + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + obs_dict: dict[str, Any] = {} + + states = self.bus.sync_read_all_states() + + for motor in self.bus.motors: + state = states.get(motor, {}) + obs_dict[f"{motor}.pos"] = state.get("position", 0.0) + obs_dict[f"{motor}.vel"] = state.get("velocity", 0.0) + obs_dict[f"{motor}.torque"] = state.get("torque", 0.0) + + # Capture images from cameras + for cam_key, cam in self.cameras.items(): + start = time.perf_counter() + obs_dict[cam_key] = cam.async_read() + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms") + + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} get_observation took: {dt_ms:.1f}ms") + + return obs_dict + + def send_action( + self, + action: RobotAction, + custom_kp: dict[str, float] | None = None, + custom_kd: dict[str, float] | None = None, + ) -> RobotAction: + """ + Send action command to robot. + + The action magnitude may be clipped based on safety limits. + + Args: + action: Dictionary with motor positions (e.g., "joint_1.pos", "joint_2.pos") + custom_kp: Optional custom kp gains per motor (e.g., {"joint_1": 120.0, "joint_2": 150.0}) + custom_kd: Optional custom kd gains per motor (e.g., {"joint_1": 1.5, "joint_2": 2.0}) + + Returns: + The action actually sent (potentially clipped) + """ + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + goal_pos = {key.removesuffix(".pos"): val for key, val in action.items() if key.endswith(".pos")} + + # Apply joint limit clipping to arm + for motor_name, position in goal_pos.items(): + if motor_name in self.config.joint_limits: + min_limit, max_limit = self.config.joint_limits[motor_name] + clipped_position = max(min_limit, min(max_limit, position)) + if clipped_position != position: + logger.debug(f"Clipped {motor_name} from {position:.2f}° to {clipped_position:.2f}°") + goal_pos[motor_name] = clipped_position + + # Cap goal position when too far away from present position. + # /!\ Slower fps expected due to reading from the follower. + if self.config.max_relative_target is not None: + present_pos = self.bus.sync_read("Present_Position") + goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()} + goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target) + + # TODO(Steven, Pepijn): Refactor writing + # Motor name to index mapping for gains + motor_index = { + "joint_1": 0, + "joint_2": 1, + "joint_3": 2, + "joint_4": 3, + "joint_5": 4, + "joint_6": 5, + "joint_7": 6, + "gripper": 7, + } + + # Use batch MIT control for arm (sends all commands, then collects responses) + commands = {} + for motor_name, position_degrees in goal_pos.items(): + idx = motor_index.get(motor_name, 0) + # Use custom gains if provided, otherwise use config defaults + if custom_kp is not None and motor_name in custom_kp: + kp = custom_kp[motor_name] + else: + kp = ( + self.config.position_kp[idx] + if isinstance(self.config.position_kp, list) + else self.config.position_kp + ) + if custom_kd is not None and motor_name in custom_kd: + kd = custom_kd[motor_name] + else: + kd = ( + self.config.position_kd[idx] + if isinstance(self.config.position_kd, list) + else self.config.position_kd + ) + commands[motor_name] = (kp, kd, position_degrees, 0.0, 0.0) + + self.bus._mit_control_batch(commands) + + return {f"{motor}.pos": val for motor, val in goal_pos.items()} + + def disconnect(self): + """Disconnect from robot.""" + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Disconnect CAN bus + self.bus.disconnect(self.config.disable_torque_on_disconnect) + + # Disconnect cameras + for cam in self.cameras.values(): + cam.disconnect() + + logger.info(f"{self} disconnected.") diff --git a/src/lerobot/robots/unitree_g1/config_unitree_g1.py b/src/lerobot/robots/unitree_g1/config_unitree_g1.py index 0b163019d..1b81214a6 100644 --- a/src/lerobot/robots/unitree_g1/config_unitree_g1.py +++ b/src/lerobot/robots/unitree_g1/config_unitree_g1.py @@ -65,3 +65,6 @@ class UnitreeG1Config(RobotConfig): # Cameras (ZMQ-based remote cameras) cameras: dict[str, CameraConfig] = field(default_factory=dict) + + # Compensates for gravity on the unitree's arms using the arm ik solver + gravity_compensation: bool = False diff --git a/src/lerobot/robots/unitree_g1/g1_utils.py b/src/lerobot/robots/unitree_g1/g1_utils.py index 3c41ee985..4e37bdcef 100644 --- a/src/lerobot/robots/unitree_g1/g1_utils.py +++ b/src/lerobot/robots/unitree_g1/g1_utils.py @@ -18,7 +18,7 @@ from enum import IntEnum # ruff: noqa: N801, N815 -NUM_MOTORS = 35 +NUM_MOTORS = 29 class G1_29_JointArmIndex(IntEnum): diff --git a/src/lerobot/robots/unitree_g1/robot_kinematic_processor.py b/src/lerobot/robots/unitree_g1/robot_kinematic_processor.py new file mode 100644 index 000000000..d086a9986 --- /dev/null +++ b/src/lerobot/robots/unitree_g1/robot_kinematic_processor.py @@ -0,0 +1,313 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import sys + +import numpy as np + +logger = logging.getLogger(__name__) +parent2_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.append(parent2_dir) + + +class WeightedMovingFilter: + def __init__(self, weights, data_size=14): + self._window_size = len(weights) + self._weights = np.array(weights) + self._data_size = data_size + self._filtered_data = np.zeros(self._data_size) + self._data_queue = [] + + def _apply_filter(self): + if len(self._data_queue) < self._window_size: + return self._data_queue[-1] + + data_array = np.array(self._data_queue) + temp_filtered_data = np.zeros(self._data_size) + for i in range(self._data_size): + temp_filtered_data[i] = np.convolve(data_array[:, i], self._weights, mode="valid")[-1] + + return temp_filtered_data + + def add_data(self, new_data): + assert len(new_data) == self._data_size + + if len(self._data_queue) > 0 and np.array_equal( + new_data, self._data_queue[-1] + ): # skip duplicate data + return + + if len(self._data_queue) >= self._window_size: + self._data_queue.pop(0) + + self._data_queue.append(new_data) + self._filtered_data = self._apply_filter() + + @property + def filtered_data(self): + return self._filtered_data + + +class G1_29_ArmIK: # noqa: N801 + def __init__(self, unit_test=False): + import casadi + import pinocchio as pin + from huggingface_hub import snapshot_download + from pinocchio import casadi as cpin + + self._pin = pin + np.set_printoptions(precision=5, suppress=True, linewidth=200) + + self.unit_test = unit_test + + self.repo_path = snapshot_download("lerobot/unitree-g1-mujoco") + urdf_path = os.path.join(self.repo_path, "assets", "g1_body29_hand14.urdf") + mesh_dir = os.path.join(self.repo_path, "assets") + + self.robot = self._pin.RobotWrapper.BuildFromURDF(urdf_path, mesh_dir) + + self.mixed_jointsToLockIDs = [ + "left_hip_pitch_joint", + "left_hip_roll_joint", + "left_hip_yaw_joint", + "left_knee_joint", + "left_ankle_pitch_joint", + "left_ankle_roll_joint", + "right_hip_pitch_joint", + "right_hip_roll_joint", + "right_hip_yaw_joint", + "right_knee_joint", + "right_ankle_pitch_joint", + "right_ankle_roll_joint", + "waist_yaw_joint", + "waist_roll_joint", + "waist_pitch_joint", + "left_hand_thumb_0_joint", + "left_hand_thumb_1_joint", + "left_hand_thumb_2_joint", + "left_hand_middle_0_joint", + "left_hand_middle_1_joint", + "left_hand_index_0_joint", + "left_hand_index_1_joint", + "right_hand_thumb_0_joint", + "right_hand_thumb_1_joint", + "right_hand_thumb_2_joint", + "right_hand_index_0_joint", + "right_hand_index_1_joint", + "right_hand_middle_0_joint", + "right_hand_middle_1_joint", + ] + + self.reduced_robot = self.robot.buildReducedRobot( + list_of_joints_to_lock=self.mixed_jointsToLockIDs, + reference_configuration=np.array([0.0] * self.robot.model.nq), + ) + + # Arm joint names in G1 motor order (G1_29_JointArmIndex) + self._arm_joint_names_g1 = [ + "left_shoulder_pitch_joint", + "left_shoulder_roll_joint", + "left_shoulder_yaw_joint", + "left_elbow_joint", + "left_wrist_roll_joint", + "left_wrist_pitch_joint", + "left_wrist_yaw_joint", + "right_shoulder_pitch_joint", + "right_shoulder_roll_joint", + "right_shoulder_yaw_joint", + "right_elbow_joint", + "right_wrist_roll_joint", + "right_wrist_pitch_joint", + "right_wrist_yaw_joint", + ] + # Pinocchio uses its own joint order in q; build index mapping. + self._arm_joint_names_pin = sorted( + self._arm_joint_names_g1, + key=lambda name: self.reduced_robot.model.idx_qs[self.reduced_robot.model.getJointId(name)], + ) + logger.info(f"Pinocchio arm joint order: {self._arm_joint_names_pin}") + self._arm_reorder_g1_to_pin = [ + self._arm_joint_names_g1.index(name) for name in self._arm_joint_names_pin + ] + # Inverse mapping to return tau in G1 motor order. + self._arm_reorder_pin_to_g1 = np.argsort(self._arm_reorder_g1_to_pin) + + self.reduced_robot.model.addFrame( + self._pin.Frame( + "L_ee", + self.reduced_robot.model.getJointId("left_wrist_yaw_joint"), + self._pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T), + self._pin.FrameType.OP_FRAME, + ) + ) + + self.reduced_robot.model.addFrame( + self._pin.Frame( + "R_ee", + self.reduced_robot.model.getJointId("right_wrist_yaw_joint"), + self._pin.SE3(np.eye(3), np.array([0.05, 0, 0]).T), + self._pin.FrameType.OP_FRAME, + ) + ) + + # Creating Casadi models and data for symbolic computing + self.cmodel = cpin.Model(self.reduced_robot.model) + self.cdata = self.cmodel.createData() + + # Creating symbolic variables + self.cq = casadi.SX.sym("q", self.reduced_robot.model.nq, 1) + self.cTf_l = casadi.SX.sym("tf_l", 4, 4) + self.cTf_r = casadi.SX.sym("tf_r", 4, 4) + cpin.framesForwardKinematics(self.cmodel, self.cdata, self.cq) + + # Get the hand joint ID and define the error function + self.L_hand_id = self.reduced_robot.model.getFrameId("L_ee") + self.R_hand_id = self.reduced_robot.model.getFrameId("R_ee") + + self.translational_error = casadi.Function( + "translational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + self.cdata.oMf[self.L_hand_id].translation - self.cTf_l[:3, 3], + self.cdata.oMf[self.R_hand_id].translation - self.cTf_r[:3, 3], + ) + ], + ) + self.rotational_error = casadi.Function( + "rotational_error", + [self.cq, self.cTf_l, self.cTf_r], + [ + casadi.vertcat( + cpin.log3(self.cdata.oMf[self.L_hand_id].rotation @ self.cTf_l[:3, :3].T), + cpin.log3(self.cdata.oMf[self.R_hand_id].rotation @ self.cTf_r[:3, :3].T), + ) + ], + ) + + # Defining the optimization problem + self.opti = casadi.Opti() + self.var_q = self.opti.variable(self.reduced_robot.model.nq) + self.var_q_last = self.opti.parameter(self.reduced_robot.model.nq) # for smooth + self.param_tf_l = self.opti.parameter(4, 4) + self.param_tf_r = self.opti.parameter(4, 4) + self.translational_cost = casadi.sumsqr( + self.translational_error(self.var_q, self.param_tf_l, self.param_tf_r) + ) + self.rotation_cost = casadi.sumsqr( + self.rotational_error(self.var_q, self.param_tf_l, self.param_tf_r) + ) + self.regularization_cost = casadi.sumsqr(self.var_q) + self.smooth_cost = casadi.sumsqr(self.var_q - self.var_q_last) + + # Setting optimization constraints and goals + self.opti.subject_to( + self.opti.bounded( + self.reduced_robot.model.lowerPositionLimit, + self.var_q, + self.reduced_robot.model.upperPositionLimit, + ) + ) + self.opti.minimize( + 50 * self.translational_cost + + self.rotation_cost + + 0.02 * self.regularization_cost + + 0.1 * self.smooth_cost + ) + + opts = { + "ipopt": {"print_level": 0, "max_iter": 50, "tol": 1e-6}, + "print_time": False, # print or not + "calc_lam_p": False, # https://github.com/casadi/casadi/wiki/FAQ:-Why-am-I-getting-%22NaN-detected%22in-my-optimization%3F + } + self.opti.solver("ipopt", opts) + + self.init_data = np.zeros(self.reduced_robot.model.nq) + self.smooth_filter = WeightedMovingFilter(np.array([0.4, 0.3, 0.2, 0.1]), 14) + + def solve_ik(self, left_wrist, right_wrist, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + if current_lr_arm_motor_q is not None: + self.init_data = current_lr_arm_motor_q + self.opti.set_initial(self.var_q, self.init_data) + + self.opti.set_value(self.param_tf_l, left_wrist) + self.opti.set_value(self.param_tf_r, right_wrist) + self.opti.set_value(self.var_q_last, self.init_data) # for smooth + + try: + self.opti.solve() + + sol_q = self.opti.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + sol_tauff = self._pin.rnea( + self.reduced_robot.model, + self.reduced_robot.data, + sol_q, + v, + np.zeros(self.reduced_robot.model.nv), + ) + + return sol_q, sol_tauff + + except Exception as e: + logger.error(f"ERROR in convergence, plotting debug info.{e}") + + sol_q = self.opti.debug.value(self.var_q) + self.smooth_filter.add_data(sol_q) + sol_q = self.smooth_filter.filtered_data + + if current_lr_arm_motor_dq is not None: + v = current_lr_arm_motor_dq * 0.0 + else: + v = (sol_q - self.init_data) * 0.0 + + self.init_data = sol_q + + logger.error( + f"sol_q:{sol_q} \nmotorstate: \n{current_lr_arm_motor_q} \nleft_pose: \n{left_wrist} \nright_pose: \n{right_wrist}" + ) + + return current_lr_arm_motor_q, np.zeros(self.reduced_robot.model.nv) + + def solve_tau(self, current_lr_arm_motor_q=None, current_lr_arm_motor_dq=None): + try: + q_g1 = np.array(current_lr_arm_motor_q, dtype=float) + if q_g1.shape[0] != len(self._arm_joint_names_g1): + raise ValueError(f"Expected {len(self._arm_joint_names_g1)} arm joints, got {q_g1.shape[0]}") + q_pin = q_g1[self._arm_reorder_g1_to_pin] + sol_tauff = self._pin.rnea( + self.reduced_robot.model, + self.reduced_robot.data, + q_pin, + np.zeros(self.reduced_robot.model.nv), + np.zeros(self.reduced_robot.model.nv), + ) + return sol_tauff[self._arm_reorder_pin_to_g1] + + except Exception as e: + logger.error(f"ERROR in convergence, plotting debug info.{e}") + return np.zeros(self.reduced_robot.model.nv) diff --git a/src/lerobot/robots/unitree_g1/unitree_g1.py b/src/lerobot/robots/unitree_g1/unitree_g1.py index fa6e0da85..01b4f330e 100644 --- a/src/lerobot/robots/unitree_g1/unitree_g1.py +++ b/src/lerobot/robots/unitree_g1/unitree_g1.py @@ -27,7 +27,8 @@ import numpy as np from lerobot.cameras.utils import make_cameras_from_configs from lerobot.envs.factory import make_env from lerobot.processor import RobotAction, RobotObservation -from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex +from lerobot.robots.unitree_g1.g1_utils import G1_29_JointArmIndex, G1_29_JointIndex +from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK from ..robot import Robot from .config_unitree_g1 import UnitreeG1Config @@ -127,6 +128,8 @@ class UnitreeG1(Robot): self.subscribe_thread = None self.remote_controller = self.RemoteController() + self.arm_ik = G1_29_ArmIK() + def _subscribe_motor_state(self): # polls robot state @ 250Hz while not self._shutdown_event.is_set(): start_time = time.time() @@ -361,6 +364,20 @@ class UnitreeG1(Robot): self.msg.motor_cmd[motor.value].kd = self.kd[motor.value] self.msg.motor_cmd[motor.value].tau = 0 + if self.config.gravity_compensation: + # Build action_np from motor commands (arm joints are indices 15-28, local indices 0-13) + action_np = np.zeros(14) + arm_start_idx = G1_29_JointArmIndex.kLeftShoulderPitch.value # 15 + for joint in G1_29_JointArmIndex: + local_idx = joint.value - arm_start_idx + action_np[local_idx] = self.msg.motor_cmd[joint.value].q + tau = self.arm_ik.solve_tau(action_np) + + # Apply tau back to motor commands + for joint in G1_29_JointArmIndex: + local_idx = joint.value - arm_start_idx + self.msg.motor_cmd[joint.value].tau = tau[local_idx] + self.msg.crc = self.crc.Crc(self.msg) self.lowcmd_publisher.Write(self.msg) return action diff --git a/src/lerobot/robots/utils.py b/src/lerobot/robots/utils.py index 27abaaa86..92da597f1 100644 --- a/src/lerobot/robots/utils.py +++ b/src/lerobot/robots/utils.py @@ -60,6 +60,14 @@ def make_robot_from_config(config: RobotConfig) -> Robot: from .reachy2 import Reachy2Robot return Reachy2Robot(config) + elif config.type == "openarm_follower": + from .openarm_follower import OpenArmFollower + + return OpenArmFollower(config) + elif config.type == "bi_openarm_follower": + from .bi_openarm_follower import BiOpenArmFollower + + return BiOpenArmFollower(config) elif config.type == "mock_robot": from tests.mocks.mock_robot import MockRobot diff --git a/src/lerobot/scripts/lerobot_calibrate.py b/src/lerobot/scripts/lerobot_calibrate.py index cbc7684d3..1b30021dd 100644 --- a/src/lerobot/scripts/lerobot_calibrate.py +++ b/src/lerobot/scripts/lerobot_calibrate.py @@ -36,23 +36,28 @@ from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraCon from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, + bi_openarm_follower, bi_so_follower, hope_jr, koch_follower, lekiwi, make_robot_from_config, omx_follower, + openarm_follower, so_follower, ) from lerobot.teleoperators import ( # noqa: F401 Teleoperator, TeleoperatorConfig, + bi_openarm_leader, bi_so_leader, homunculus, koch_leader, make_teleoperator_from_config, omx_leader, + openarm_leader, so_leader, + unitree_g1, ) from lerobot.utils.import_utils import register_third_party_plugins from lerobot.utils.utils import init_logging @@ -81,8 +86,11 @@ def calibrate(cfg: CalibrateConfig): device = make_teleoperator_from_config(cfg.device) device.connect(calibrate=False) - device.calibrate() - device.disconnect() + + try: + device.calibrate() + finally: + device.disconnect() def main(): diff --git a/src/lerobot/scripts/lerobot_edit_dataset.py b/src/lerobot/scripts/lerobot_edit_dataset.py index 4ba6ce44f..2ca9c520d 100644 --- a/src/lerobot/scripts/lerobot_edit_dataset.py +++ b/src/lerobot/scripts/lerobot_edit_dataset.py @@ -18,7 +18,7 @@ Edit LeRobot datasets using various transformation tools. This script allows you to delete episodes, split datasets, merge datasets, -remove features, and convert image datasets to video format. +remove features, modify tasks, and convert image datasets to video format. When new_repo_id is specified, creates a new dataset. Usage Examples: @@ -66,6 +66,25 @@ Remove camera feature: --operation.type remove_feature \ --operation.feature_names "['observation.images.top']" +Modify tasks - set a single task for all episodes (WARNING: modifies in-place): + python -m lerobot.scripts.lerobot_edit_dataset \ + --repo_id lerobot/pusht \ + --operation.type modify_tasks \ + --operation.new_task "Pick up the cube and place it" + +Modify tasks - set different tasks for specific episodes (WARNING: modifies in-place): + python -m lerobot.scripts.lerobot_edit_dataset \ + --repo_id lerobot/pusht \ + --operation.type modify_tasks \ + --operation.episode_tasks '{"0": "Task A", "1": "Task B", "2": "Task A"}' + +Modify tasks - set default task with overrides for specific episodes (WARNING: modifies in-place): + python -m lerobot.scripts.lerobot_edit_dataset \ + --repo_id lerobot/pusht \ + --operation.type modify_tasks \ + --operation.new_task "Default task" \ + --operation.episode_tasks '{"5": "Special task for episode 5"}' + Convert image dataset to video format and save locally: python -m lerobot.scripts.lerobot_edit_dataset \ --repo_id lerobot/pusht_image \ @@ -100,6 +119,7 @@ from lerobot.datasets.dataset_tools import ( convert_image_to_video_dataset, delete_episodes, merge_datasets, + modify_tasks, remove_feature, split_dataset, ) @@ -132,6 +152,13 @@ class RemoveFeatureConfig: feature_names: list[str] | None = None +@dataclass +class ModifyTasksConfig: + type: str = "modify_tasks" + new_task: str | None = None + episode_tasks: dict[str, str] | None = None + + @dataclass class ConvertImageToVideoConfig: type: str = "convert_image_to_video" @@ -151,7 +178,12 @@ class ConvertImageToVideoConfig: class EditDatasetConfig: repo_id: str operation: ( - DeleteEpisodesConfig | SplitConfig | MergeConfig | RemoveFeatureConfig | ConvertImageToVideoConfig + DeleteEpisodesConfig + | SplitConfig + | MergeConfig + | RemoveFeatureConfig + | ModifyTasksConfig + | ConvertImageToVideoConfig ) root: str | None = None new_repo_id: str | None = None @@ -296,6 +328,48 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None: LeRobotDataset(output_repo_id, root=output_dir).push_to_hub() +def handle_modify_tasks(cfg: EditDatasetConfig) -> None: + if not isinstance(cfg.operation, ModifyTasksConfig): + raise ValueError("Operation config must be ModifyTasksConfig") + + new_task = cfg.operation.new_task + episode_tasks_raw = cfg.operation.episode_tasks + + if new_task is None and episode_tasks_raw is None: + raise ValueError("Must specify at least one of new_task or episode_tasks for modify_tasks operation") + + # Warn about in-place modification behavior + if cfg.new_repo_id is not None: + logging.warning("modify_tasks modifies datasets in-place. The --new_repo_id parameter is ignored.") + + dataset = LeRobotDataset(cfg.repo_id, root=cfg.root) + logging.warning(f"Modifying dataset in-place at {dataset.root}. Original data will be overwritten.") + + # Convert episode_tasks keys from string to int if needed (CLI passes strings) + episode_tasks: dict[int, str] | None = None + if episode_tasks_raw is not None: + episode_tasks = {int(k): v for k, v in episode_tasks_raw.items()} + + logging.info(f"Modifying tasks in {cfg.repo_id}") + if new_task: + logging.info(f" Default task: '{new_task}'") + if episode_tasks: + logging.info(f" Episode-specific tasks: {episode_tasks}") + + modified_dataset = modify_tasks( + dataset, + new_task=new_task, + episode_tasks=episode_tasks, + ) + + logging.info(f"Dataset modified at {dataset.root}") + logging.info(f"Tasks: {list(modified_dataset.meta.tasks.index)}") + + if cfg.push_to_hub: + logging.info(f"Pushing to hub as {cfg.repo_id}") + modified_dataset.push_to_hub() + + def handle_convert_image_to_video(cfg: EditDatasetConfig) -> None: # Note: Parser may create any config type with the right fields, so we access fields directly # instead of checking isinstance() @@ -371,12 +445,14 @@ def edit_dataset(cfg: EditDatasetConfig) -> None: handle_merge(cfg) elif operation_type == "remove_feature": handle_remove_feature(cfg) + elif operation_type == "modify_tasks": + handle_modify_tasks(cfg) elif operation_type == "convert_image_to_video": handle_convert_image_to_video(cfg) else: raise ValueError( f"Unknown operation type: {operation_type}\n" - f"Available operations: delete_episodes, split, merge, remove_feature, convert_to_video" + f"Available operations: delete_episodes, split, merge, remove_feature, modify_tasks, convert_image_to_video" ) diff --git a/src/lerobot/scripts/lerobot_find_joint_limits.py b/src/lerobot/scripts/lerobot_find_joint_limits.py index 20bbc8615..082d11803 100644 --- a/src/lerobot/scripts/lerobot_find_joint_limits.py +++ b/src/lerobot/scripts/lerobot_find_joint_limits.py @@ -44,19 +44,23 @@ import numpy as np from lerobot.model.kinematics import RobotKinematics from lerobot.robots import ( # noqa: F401 RobotConfig, + bi_openarm_follower, bi_so_follower, koch_follower, make_robot_from_config, omx_follower, + openarm_follower, so_follower, ) from lerobot.teleoperators import ( # noqa: F401 TeleoperatorConfig, + bi_openarm_leader, bi_so_leader, gamepad, koch_leader, make_teleoperator_from_config, omx_leader, + openarm_leader, so_leader, ) from lerobot.utils.robot_utils import precise_sleep diff --git a/src/lerobot/scripts/lerobot_record.py b/src/lerobot/scripts/lerobot_record.py index f03776989..0b39e6fff 100644 --- a/src/lerobot/scripts/lerobot_record.py +++ b/src/lerobot/scripts/lerobot_record.py @@ -98,26 +98,31 @@ from lerobot.processor.rename_processor import rename_stats from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, + bi_openarm_follower, bi_so_follower, earthrover_mini_plus, hope_jr, koch_follower, make_robot_from_config, omx_follower, + openarm_follower, reachy2, so_follower, - unitree_g1, + unitree_g1 as unitree_g1_robot, ) from lerobot.teleoperators import ( # noqa: F401 Teleoperator, TeleoperatorConfig, + bi_openarm_leader, bi_so_leader, homunculus, koch_leader, make_teleoperator_from_config, omx_leader, + openarm_leader, reachy2_teleoperator, so_leader, + unitree_g1, ) from lerobot.teleoperators.keyboard.teleop_keyboard import KeyboardTeleop from lerobot.utils.constants import ACTION, OBS_STR diff --git a/src/lerobot/scripts/lerobot_replay.py b/src/lerobot/scripts/lerobot_replay.py index 49c06d643..c9a559d07 100644 --- a/src/lerobot/scripts/lerobot_replay.py +++ b/src/lerobot/scripts/lerobot_replay.py @@ -53,12 +53,14 @@ from lerobot.processor import ( from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, + bi_openarm_follower, bi_so_follower, earthrover_mini_plus, hope_jr, koch_follower, make_robot_from_config, omx_follower, + openarm_follower, reachy2, so_follower, unitree_g1, @@ -108,25 +110,26 @@ def replay(cfg: ReplayConfig): robot.connect() - log_say("Replaying episode", cfg.play_sounds, blocking=True) - for idx in range(len(episode_frames)): - start_episode_t = time.perf_counter() + try: + log_say("Replaying episode", cfg.play_sounds, blocking=True) + for idx in range(len(episode_frames)): + start_episode_t = time.perf_counter() - action_array = actions[idx][ACTION] - action = {} - for i, name in enumerate(dataset.features[ACTION]["names"]): - action[name] = action_array[i] + action_array = actions[idx][ACTION] + action = {} + for i, name in enumerate(dataset.features[ACTION]["names"]): + action[name] = action_array[i] - robot_obs = robot.get_observation() + robot_obs = robot.get_observation() - processed_action = robot_action_processor((action, robot_obs)) + processed_action = robot_action_processor((action, robot_obs)) - _ = robot.send_action(processed_action) + _ = robot.send_action(processed_action) - dt_s = time.perf_counter() - start_episode_t - precise_sleep(max(1 / dataset.fps - dt_s, 0.0)) - - robot.disconnect() + dt_s = time.perf_counter() - start_episode_t + precise_sleep(max(1 / dataset.fps - dt_s, 0.0)) + finally: + robot.disconnect() def main(): diff --git a/src/lerobot/scripts/lerobot_teleoperate.py b/src/lerobot/scripts/lerobot_teleoperate.py index 18d8863d6..b6aa4a750 100644 --- a/src/lerobot/scripts/lerobot_teleoperate.py +++ b/src/lerobot/scripts/lerobot_teleoperate.py @@ -70,18 +70,22 @@ from lerobot.processor import ( from lerobot.robots import ( # noqa: F401 Robot, RobotConfig, + bi_openarm_follower, bi_so_follower, earthrover_mini_plus, hope_jr, koch_follower, make_robot_from_config, omx_follower, + openarm_follower, reachy2, so_follower, + unitree_g1 as unitree_g1_robot, ) from lerobot.teleoperators import ( # noqa: F401 Teleoperator, TeleoperatorConfig, + bi_openarm_leader, bi_so_leader, gamepad, homunculus, @@ -89,8 +93,10 @@ from lerobot.teleoperators import ( # noqa: F401 koch_leader, make_teleoperator_from_config, omx_leader, + openarm_leader, reachy2_teleoperator, so_leader, + unitree_g1, ) from lerobot.utils.import_utils import register_third_party_plugins from lerobot.utils.robot_utils import precise_sleep diff --git a/src/lerobot/teleoperators/bi_openarm_leader/__init__.py b/src/lerobot/teleoperators/bi_openarm_leader/__init__.py new file mode 100644 index 000000000..fe728b826 --- /dev/null +++ b/src/lerobot/teleoperators/bi_openarm_leader/__init__.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .bi_openarm_leader import BiOpenArmLeader +from .config_bi_openarm_leader import BiOpenArmLeaderConfig + +__all__ = ["BiOpenArmLeader", "BiOpenArmLeaderConfig"] diff --git a/src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py b/src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py new file mode 100644 index 000000000..c4383293f --- /dev/null +++ b/src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +from functools import cached_property + +from lerobot.processor import RobotAction +from lerobot.teleoperators.openarm_leader import OpenArmLeaderConfig + +from ..openarm_leader import OpenArmLeader +from ..teleoperator import Teleoperator +from .config_bi_openarm_leader import BiOpenArmLeaderConfig + +logger = logging.getLogger(__name__) + + +class BiOpenArmLeader(Teleoperator): + """ + Bimanual OpenArm Leader Arms + """ + + config_class = BiOpenArmLeaderConfig + name = "bi_openarm_leader" + + def __init__(self, config: BiOpenArmLeaderConfig): + super().__init__(config) + self.config = config + + left_arm_config = OpenArmLeaderConfig( + id=f"{config.id}_left" if config.id else None, + calibration_dir=config.calibration_dir, + port=config.left_arm_config.port, + can_interface=config.left_arm_config.can_interface, + use_can_fd=config.left_arm_config.use_can_fd, + can_bitrate=config.left_arm_config.can_bitrate, + can_data_bitrate=config.left_arm_config.can_data_bitrate, + motor_config=config.left_arm_config.motor_config, + manual_control=config.left_arm_config.manual_control, + position_kd=config.left_arm_config.position_kd, + position_kp=config.left_arm_config.position_kp, + ) + + right_arm_config = OpenArmLeaderConfig( + id=f"{config.id}_right" if config.id else None, + calibration_dir=config.calibration_dir, + port=config.right_arm_config.port, + can_interface=config.right_arm_config.can_interface, + use_can_fd=config.right_arm_config.use_can_fd, + can_bitrate=config.right_arm_config.can_bitrate, + can_data_bitrate=config.right_arm_config.can_data_bitrate, + motor_config=config.right_arm_config.motor_config, + manual_control=config.right_arm_config.manual_control, + position_kd=config.right_arm_config.position_kd, + position_kp=config.right_arm_config.position_kp, + ) + + self.left_arm = OpenArmLeader(left_arm_config) + self.right_arm = OpenArmLeader(right_arm_config) + + @cached_property + def action_features(self) -> dict[str, type]: + left_arm_features = self.left_arm.action_features + right_arm_features = self.right_arm.action_features + + return { + **{f"left_{k}": v for k, v in left_arm_features.items()}, + **{f"right_{k}": v for k, v in right_arm_features.items()}, + } + + @cached_property + def feedback_features(self) -> dict[str, type]: + return {} + + @property + def is_connected(self) -> bool: + return self.left_arm.is_connected and self.right_arm.is_connected + + def connect(self, calibrate: bool = True) -> None: + self.left_arm.connect(calibrate) + self.right_arm.connect(calibrate) + + @property + def is_calibrated(self) -> bool: + return self.left_arm.is_calibrated and self.right_arm.is_calibrated + + def calibrate(self) -> None: + self.left_arm.calibrate() + self.right_arm.calibrate() + + def configure(self) -> None: + self.left_arm.configure() + self.right_arm.configure() + + def setup_motors(self) -> None: + raise NotImplementedError( + "Motor ID configuration is typically done via manufacturer tools for CAN motors." + ) + + def get_action(self) -> RobotAction: + action_dict = {} + + # Add "left_" prefix + left_action = self.left_arm.get_action() + action_dict.update({f"left_{key}": value for key, value in left_action.items()}) + + # Add "right_" prefix + right_action = self.right_arm.get_action() + action_dict.update({f"right_{key}": value for key, value in right_action.items()}) + + return action_dict + + def send_feedback(self, feedback: dict[str, float]) -> None: + # TODO: Implement force feedback + raise NotImplementedError + + def disconnect(self) -> None: + self.left_arm.disconnect() + self.right_arm.disconnect() diff --git a/src/lerobot/teleoperators/bi_openarm_leader/config_bi_openarm_leader.py b/src/lerobot/teleoperators/bi_openarm_leader/config_bi_openarm_leader.py new file mode 100644 index 000000000..39fc90add --- /dev/null +++ b/src/lerobot/teleoperators/bi_openarm_leader/config_bi_openarm_leader.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from lerobot.teleoperators.openarm_leader import OpenArmLeaderConfigBase + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("bi_openarm_leader") +@dataclass +class BiOpenArmLeaderConfig(TeleoperatorConfig): + """Configuration class for Bi OpenArm Follower robots.""" + + left_arm_config: OpenArmLeaderConfigBase + right_arm_config: OpenArmLeaderConfigBase diff --git a/src/lerobot/teleoperators/openarm_leader/__init__.py b/src/lerobot/teleoperators/openarm_leader/__init__.py new file mode 100644 index 000000000..172cf8228 --- /dev/null +++ b/src/lerobot/teleoperators/openarm_leader/__init__.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .config_openarm_leader import OpenArmLeaderConfig, OpenArmLeaderConfigBase +from .openarm_leader import OpenArmLeader + +__all__ = ["OpenArmLeader", "OpenArmLeaderConfig", "OpenArmLeaderConfigBase"] diff --git a/src/lerobot/teleoperators/openarm_leader/config_openarm_leader.py b/src/lerobot/teleoperators/openarm_leader/config_openarm_leader.py new file mode 100644 index 000000000..4b12fe730 --- /dev/null +++ b/src/lerobot/teleoperators/openarm_leader/config_openarm_leader.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from ..config import TeleoperatorConfig + + +@dataclass +class OpenArmLeaderConfigBase: + """Base configuration for the OpenArms leader/teleoperator with Damiao motors.""" + + # CAN interfaces - one per arm + # Arm CAN interface (e.g., "can3") + # Linux: "can0", "can1", etc. + port: str + + # CAN interface type: "socketcan" (Linux), "slcan" (serial), or "auto" (auto-detect) + can_interface: str = "socketcan" + + # CAN FD settings (OpenArms uses CAN FD by default) + use_can_fd: bool = True + can_bitrate: int = 1000000 # Nominal bitrate (1 Mbps) + can_data_bitrate: int = 5000000 # Data bitrate for CAN FD (5 Mbps) + + # Motor configuration for OpenArms (7 DOF per arm) + # Maps motor names to (send_can_id, recv_can_id, motor_type) + # Based on: https://docs.openarm.dev/software/setup/configure-test + # OpenArms uses 4 types of motors: + # - DM8009 (DM-J8009P-2EC) for shoulders (high torque) + # - DM4340P and DM4340 for shoulder rotation and elbow + # - DM4310 (DM-J4310-2EC V1.1) for wrist and gripper + motor_config: dict[str, tuple[int, int, str]] = field( + default_factory=lambda: { + "joint_1": (0x01, 0x11, "dm8009"), # J1 - Shoulder pan (DM8009) + "joint_2": (0x02, 0x12, "dm8009"), # J2 - Shoulder lift (DM8009) + "joint_3": (0x03, 0x13, "dm4340"), # J3 - Shoulder rotation (DM4340) + "joint_4": (0x04, 0x14, "dm4340"), # J4 - Elbow flex (DM4340) + "joint_5": (0x05, 0x15, "dm4310"), # J5 - Wrist roll (DM4310) + "joint_6": (0x06, 0x16, "dm4310"), # J6 - Wrist pitch (DM4310) + "joint_7": (0x07, 0x17, "dm4310"), # J7 - Wrist rotation (DM4310) + "gripper": (0x08, 0x18, "dm4310"), # J8 - Gripper (DM4310) + } + ) + + # Torque mode settings for manual control + # When enabled, motors have torque disabled for manual movement + manual_control: bool = True + + # TODO(Steven, Pepijn): Not used ... ? + # MIT control parameters (used when manual_control=False for torque control) + # List of 8 values: [joint_1, joint_2, joint_3, joint_4, joint_5, joint_6, joint_7, gripper] + position_kp: list[float] = field( + default_factory=lambda: [240.0, 240.0, 240.0, 240.0, 24.0, 31.0, 25.0, 16.0] + ) + position_kd: list[float] = field(default_factory=lambda: [3.0, 3.0, 3.0, 3.0, 0.2, 0.2, 0.2, 0.2]) + + +@TeleoperatorConfig.register_subclass("openarm_leader") +@dataclass +class OpenArmLeaderConfig(TeleoperatorConfig, OpenArmLeaderConfigBase): + pass diff --git a/src/lerobot/teleoperators/openarm_leader/openarm_leader.py b/src/lerobot/teleoperators/openarm_leader/openarm_leader.py new file mode 100644 index 000000000..edf4d7090 --- /dev/null +++ b/src/lerobot/teleoperators/openarm_leader/openarm_leader.py @@ -0,0 +1,225 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from typing import Any + +from lerobot.motors import Motor, MotorCalibration, MotorNormMode +from lerobot.motors.damiao import DamiaoMotorsBus +from lerobot.processor import RobotAction +from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..teleoperator import Teleoperator +from .config_openarm_leader import OpenArmLeaderConfig + +logger = logging.getLogger(__name__) + + +class OpenArmLeader(Teleoperator): + """ + OpenArm Leader/Teleoperator Arm with Damiao motors. + + This teleoperator uses CAN bus communication to read positions from + Damiao motors that are manually moved (torque disabled). + """ + + config_class = OpenArmLeaderConfig + name = "openarm_leader" + + def __init__(self, config: OpenArmLeaderConfig): + super().__init__(config) + self.config = config + + # Arm motors + motors: dict[str, Motor] = {} + for motor_name, (send_id, recv_id, motor_type_str) in config.motor_config.items(): + motor = Motor( + send_id, motor_type_str, MotorNormMode.DEGREES + ) # Always use degrees for Damiao motors + motor.recv_id = recv_id + motor.motor_type_str = motor_type_str + motors[motor_name] = motor + + self.bus = DamiaoMotorsBus( + port=self.config.port, + motors=motors, + calibration=self.calibration, + can_interface=self.config.can_interface, + use_can_fd=self.config.use_can_fd, + bitrate=self.config.can_bitrate, + data_bitrate=self.config.can_data_bitrate if self.config.use_can_fd else None, + ) + + @property + def action_features(self) -> dict[str, type]: + """Features produced by this teleoperator.""" + features: dict[str, type] = {} + for motor in self.bus.motors: + features[f"{motor}.pos"] = float + features[f"{motor}.vel"] = float + features[f"{motor}.torque"] = float + return features + + @property + def feedback_features(self) -> dict[str, type]: + """Feedback features (not implemented for OpenArms).""" + return {} + + @property + def is_connected(self) -> bool: + """Check if teleoperator is connected.""" + return self.bus.is_connected + + def connect(self, calibrate: bool = True) -> None: + """ + Connect to the teleoperator. + + For manual control, we disable torque after connecting so the + arm can be moved by hand. + """ + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + # Connect to CAN bus + logger.info(f"Connecting arm on {self.config.port}...") + self.bus.connect() + + # Run calibration if needed + if not self.is_calibrated and calibrate: + logger.info( + "Mismatch between calibration values in the motor and the calibration file or no calibration file found" + ) + self.calibrate() + + self.configure() + + if self.is_calibrated: + self.bus.set_zero_position() + + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + """Check if teleoperator is calibrated.""" + return self.bus.is_calibrated + + def calibrate(self) -> None: + """ + Run calibration procedure for OpenArms leader. + + The calibration procedure: + 1. Disable torque (if not already disabled) + 2. Ask user to position arm in zero position (hanging with gripper closed) + 3. Set this as zero position + 4. Record range of motion for each joint + 5. Save calibration + """ + if self.calibration: + # Calibration file exists, ask user whether to use it or run new calibration + user_input = input( + f"Press ENTER to use provided calibration file associated with the id {self.id}, or type 'c' and press ENTER to run calibration: " + ) + if user_input.strip().lower() != "c": + logger.info(f"Writing calibration file associated with the id {self.id} to the motors") + self.bus.write_calibration(self.calibration) + return + + logger.info(f"\nRunning calibration for {self}") + self.bus.disable_torque() + + # Step 1: Set zero position + input( + "\nCalibration: Set Zero Position)\n" + "Position the arm in the following configuration:\n" + " - Arm hanging straight down\n" + " - Gripper closed\n" + "Press ENTER when ready..." + ) + + # Set current position as zero for all motors + self.bus.set_zero_position() + logger.info("Arm zero position set.") + + logger.info("Setting range: -90° to +90° by default for all joints") + # TODO(Steven, Pepijn): Check if MotorCalibration is actually needed here given that we only use Degrees + for motor_name, motor in self.bus.motors.items(): + self.calibration[motor_name] = MotorCalibration( + id=motor.id, + drive_mode=0, + homing_offset=0, + range_min=-90, + range_max=90, + ) + + self.bus.write_calibration(self.calibration) + self._save_calibration() + print(f"Calibration saved to {self.calibration_fpath}") + + def configure(self) -> None: + """ + Configure motors for manual teleoperation. + + For manual control, we disable torque so the arm can be moved by hand. + """ + + return self.bus.disable_torque() if self.config.manual_control else self.bus.configure_motors() + + def setup_motors(self) -> None: + raise NotImplementedError( + "Motor ID configuration is typically done via manufacturer tools for CAN motors." + ) + + def get_action(self) -> RobotAction: + """ + Get current action from the leader arm. + + This is the main method for teleoperators - it reads the current state + of the leader arm and returns it as an action that can be sent to a follower. + + Reads all motor states (pos/vel/torque) in one CAN refresh cycle. + """ + start = time.perf_counter() + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + action_dict: dict[str, Any] = {} + + # Use sync_read_all_states to get pos/vel/torque in one go + states = self.bus.sync_read_all_states() + for motor in self.bus.motors: + state = states.get(motor, {}) + action_dict[f"{motor}.pos"] = state.get("position") + action_dict[f"{motor}.vel"] = state.get("velocity") + action_dict[f"{motor}.torque"] = state.get("torque") + + dt_ms = (time.perf_counter() - start) * 1e3 + logger.debug(f"{self} read state: {dt_ms:.1f}ms") + + return action_dict + + def send_feedback(self, feedback: dict[str, float]) -> None: + raise NotImplementedError("Feedback is not yet implemented for OpenArm leader.") + + def disconnect(self) -> None: + """Disconnect from teleoperator.""" + if not self.is_connected: + raise DeviceNotConnectedError(f"{self} is not connected.") + + # Disconnect CAN bus + # For manual control, ensure torque is disabled before disconnecting + self.bus.disconnect(disable_torque=self.config.manual_control) + logger.info(f"{self} disconnected.") diff --git a/src/lerobot/teleoperators/unitree_g1/__init__.py b/src/lerobot/teleoperators/unitree_g1/__init__.py new file mode 100644 index 000000000..45955a0e2 --- /dev/null +++ b/src/lerobot/teleoperators/unitree_g1/__init__.py @@ -0,0 +1,21 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .config_unitree_g1 import ExoskeletonArmPortConfig, UnitreeG1TeleoperatorConfig +from .exo_calib import ExoskeletonCalibration, ExoskeletonJointCalibration +from .exo_ik import ExoskeletonIKHelper +from .exo_serial import ExoskeletonArm +from .unitree_g1 import UnitreeG1Teleoperator diff --git a/src/lerobot/teleoperators/unitree_g1/config_unitree_g1.py b/src/lerobot/teleoperators/unitree_g1/config_unitree_g1.py new file mode 100644 index 000000000..66c4e7f31 --- /dev/null +++ b/src/lerobot/teleoperators/unitree_g1/config_unitree_g1.py @@ -0,0 +1,37 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass, field + +from ..config import TeleoperatorConfig + + +@dataclass +class ExoskeletonArmPortConfig: + """Serial port configuration for individual exoskeleton arm.""" + + port: str = "" + baud_rate: int = 115200 + + +@TeleoperatorConfig.register_subclass("unitree_g1") +@dataclass +class UnitreeG1TeleoperatorConfig(TeleoperatorConfig): + left_arm_config: ExoskeletonArmPortConfig = field(default_factory=ExoskeletonArmPortConfig) + right_arm_config: ExoskeletonArmPortConfig = field(default_factory=ExoskeletonArmPortConfig) + + # Frozen joints (comma-separated joint names that won't be moved by IK) + frozen_joints: str = "" diff --git a/src/lerobot/teleoperators/unitree_g1/exo_calib.py b/src/lerobot/teleoperators/unitree_g1/exo_calib.py new file mode 100644 index 000000000..2927a1b55 --- /dev/null +++ b/src/lerobot/teleoperators/unitree_g1/exo_calib.py @@ -0,0 +1,446 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +This module handles calibration of hall effect sensors used in the exoskeleton. +Each joint has a pair of ADC channels outputting sin and cos values that trace an ellipse +as the joint rotates due to imprecision in magnet/sensor placement. We fit this ellipse to a unit circle, +and calculate arctan2 of the unit circle to get the joint angle. +We then store the ellipse parameters and the zero offset for each joint to be used at runtime. +""" + +import json +import logging +import time +from collections import deque +from dataclasses import dataclass, field +from pathlib import Path + +import numpy as np +import serial + +logger = logging.getLogger(__name__) + + +# exoskeleton joint names -> ADC channel pairs. TODO: add wrist pitch and wrist yaw +JOINTS = { + "shoulder_pitch": (0, 1), + "shoulder_yaw": (2, 3), + "shoulder_roll": (4, 5), + "elbow_flex": (6, 7), + "wrist_roll": (14, 15), +} + + +@dataclass +class ExoskeletonJointCalibration: + name: str # joint name + center_fit: list[float] # center of the ellipse + T: list[list[float]] # 2x2 transformation matrix + zero_offset: float = 0.0 # angle at neutral pose + + +@dataclass +class ExoskeletonCalibration: + """Full calibration data for an exoskeleton arm.""" + + version: int = 2 + side: str = "" + adc_max: int = 2**12 - 1 + joints: list[ExoskeletonJointCalibration] = field(default_factory=list) + + def to_dict(self) -> dict: + return { + "version": self.version, + "side": self.side, + "adc_max": self.adc_max, + "joints": [ + { + "name": j.name, + "center_fit": j.center_fit, + "T": j.T, + "zero_offset": j.zero_offset, + } + for j in self.joints + ], + } + + @classmethod + def from_dict(cls, data: dict) -> "ExoskeletonCalibration": + joints = [ + ExoskeletonJointCalibration( + name=j["name"], + center_fit=j["center_fit"], + T=j["T"], + zero_offset=j.get("zero_offset", 0.0), + ) + for j in data.get("joints", []) + ] + return cls( + version=data.get("version", 2), + side=data.get("side", ""), + adc_max=data.get("adc_max", 2**12 - 1), + joints=joints, + ) + + +@dataclass(frozen=True) +class CalibParams: + fit_every: float = 0.15 + min_fit_points: int = 60 + fit_window: int = 900 + max_fit_points: int = 300 + trim_low: float = 0.05 + trim_high: float = 0.95 + median_window: int = 5 + history: int = 3500 + draw_hz: float = 120.0 + sample_count: int = 50 + + +def normalize_angle(angle: float) -> float: + while angle > np.pi: + angle -= 2 * np.pi + while angle < -np.pi: + angle += 2 * np.pi + return angle + + +def joint_z_and_angle(raw16: list[int], j: ExoskeletonJointCalibration) -> tuple[np.ndarray, float]: + """ + Applies calibration to each joint: raw → centered → ellipse-to-circle → angle. + """ + pair = JOINTS[j.name] + s, c = raw16[pair[0]], raw16[pair[1]] # get sin and cos + p = np.array([float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2]) # center the raw values + z = np.asarray(j.T) @ ( + p - np.asarray(j.center_fit) + ) # center the ellipse and invert the transformation matrix to get unit circle coords + ang = float(np.arctan2(z[1], z[0])) - j.zero_offset # calculate the anvgle and apply the zero offset + return z, normalize_angle(-ang) # ensure range is [-pi, pi] + + +def exo_raw_to_angles(raw16: list[int], calib: ExoskeletonCalibration) -> dict[str, float]: + """Convert raw sensor readings to joint angles using calibration.""" + return {j.name: joint_z_and_angle(raw16, j)[1] for j in calib.joints} + + +def run_exo_calibration( + ser: serial.Serial, + side: str, + save_path: Path, + params: CalibParams | None = None, +) -> ExoskeletonCalibration: + """ + Run interactive calibration for an exoskeleton arm. + """ + try: + import cv2 + import matplotlib.pyplot as plt + except ImportError as e: + raise ImportError( + "Calibration requires matplotlib and opencv-python. " + "Install with: pip install matplotlib opencv-python" + ) from e + + from .exo_serial import read_raw_from_serial + + params = params or CalibParams() + joint_list = list(JOINTS.items()) # Convert dict to list for indexing + logger.info(f"Starting calibration for {side} exoskeleton arm") + + def running_median(win: deque) -> float: + return float(np.median(np.fromiter(win, dtype=float))) + + def read_joint_point(raw16: list[int], pair: tuple[int, int]): + s, c = raw16[pair[0]], raw16[pair[1]] + return float(c) - (2**12 - 1) / 2, float(s) - (2**12 - 1) / 2, float(s), float(c) + + def select_fit_subset(xs, ys): + """Select and filter points for ellipse fitting. Trims outliers by radius and downsamples.""" + n = min(params.fit_window, len(xs)) + if n <= 0: + return None, None + x = np.asarray(list(xs)[-n:], dtype=float) # most recent n samples + y = np.asarray(list(ys)[-n:], dtype=float) + r = np.sqrt(x * x + y * y) # radius from origin + if len(r) >= 20: + lo, hi = np.quantile(r, params.trim_low), np.quantile(r, params.trim_high) # outlier bounds + keep = (r >= lo) & (r <= hi) + x, y = x[keep], y[keep] # remove outliers + if len(x) > params.max_fit_points: + idx = np.linspace(0, len(x) - 1, params.max_fit_points).astype(int) # downsample evenly + x, y = x[idx], y[idx] + return x, y + + def fit_ellipse_opencv(x, y): + """Fit ellipse to (x,y) points using OpenCV. Returns center, axes, rotation matrix, and outline.""" + x, y = np.asarray(x, dtype=float), np.asarray(y, dtype=float) + if len(x) < 5: + return None + pts = np.stack([x, y], axis=1).astype(np.float32).reshape(-1, 1, 2) + try: + (xc, yc), (w, h), angle_deg = cv2.fitEllipse(pts) # returns center, axes, rotation in degrees + except cv2.error: + return None + a, b = float(w) * 0.5, float(h) * 0.5 # get ellipse major and minor semi-axes + phi = np.deg2rad(float(angle_deg)) # to rad + if b > a: # ensure major axis is a + a, b = b, a + phi += np.pi / 2.0 + if not np.isfinite(a) or not np.isfinite(b) or a <= 1e-6 or b <= 1e-6: + return None + cp, sp = float(np.cos(phi)), float(np.sin(phi)) # + rot = np.array([[cp, -sp], [sp, cp]], dtype=float) # 2x2 rotation matrix + center = np.array([float(xc), float(yc)], dtype=float) # offset vector + tt = np.linspace(0, 2 * np.pi, 360) + outline = (rot @ np.stack([a * np.cos(tt), b * np.sin(tt)])).T + center # for viz + return {"center": center, "a": a, "b": b, "R": rot, "ex": outline[:, 0], "ey": outline[:, 1]} + + # Setup matplotlib + plt.ion() + fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 6)) + ax0.set_xlabel("cos - center") + ax0.set_ylabel("sin - center") + ax0.grid(True, alpha=0.25) + ax0.set_aspect("equal", adjustable="box") + ax1.set_title("Unit circle + angle") + ax1.set_xlabel("x") + ax1.set_ylabel("y") + ax1.grid(True, alpha=0.25) + ax1.set_aspect("equal", adjustable="box") + tt = np.linspace(0, 2 * np.pi, 360) + ax1.plot(np.cos(tt), np.sin(tt), "k-", linewidth=1) + ax0.set_xlim(-2200, 2200) + ax0.set_ylim(-2200, 2200) + ax1.set_xlim(-1.4, 1.4) + ax1.set_ylim(-1.4, 1.4) + + sc0 = ax0.scatter([], [], s=6, animated=True) + (ell_line,) = ax0.plot([], [], "r-", linewidth=2, animated=True) + sc1 = ax1.scatter([], [], s=6, animated=True) + (radius_line,) = ax1.plot([], [], "g-", linewidth=2, animated=True) + angle_text = ax1.text( + 0.02, 0.98, "", transform=ax1.transAxes, va="top", ha="left", fontsize=12, animated=True + ) + + fig.canvas.draw() + bg0 = fig.canvas.copy_from_bbox(ax0.bbox) + bg1 = fig.canvas.copy_from_bbox(ax1.bbox) + + # State + joints_out = [] + joint_idx = 0 + phase = "ellipse" + advance_requested = False + zero_samples = [] + + def on_key(event): + nonlocal advance_requested + if event.key in ("n", "N", "enter", " "): + advance_requested = True + + fig.canvas.mpl_connect("key_press_event", on_key) + + def reset_state(): + return { + "xs": deque(maxlen=params.history), + "ys": deque(maxlen=params.history), + "xu": deque(maxlen=params.history), + "yu": deque(maxlen=params.history), + "win_s": deque(maxlen=params.median_window), + "win_c": deque(maxlen=params.median_window), + "ellipse_cache": None, + "T": None, + "center_fit": None, + "have_transform": False, + "latest_z": None, + "last_fit": 0.0, + } + + state = reset_state() + last_draw = 0.0 + name, pair = joint_list[joint_idx] + fig.canvas.manager.set_window_title(f"[{joint_idx + 1}/{len(joint_list)}] {name} - ELLIPSE") + ax0.set_title(f"{name} raw (filtered)") + logger.info(f"[{joint_idx + 1}/{len(joint_list)}] Calibrating {name}") + logger.info("Step 1: Move joint around to map ellipse, then press 'n'") + + try: + while plt.fignum_exists(fig.number): + name, pair = joint_list[joint_idx] + + # Handles calibration GUI state: ellipse → zero_pose → next joint -> ellipse -> ... + if phase == "ellipse" and advance_requested and state["have_transform"]: + joints_out.append( + { + "name": name, + "center_fit": state["center_fit"].tolist(), + "T": state["T"].tolist(), + } + ) + logger.info(f" -> Ellipse saved for {name}") + phase, zero_samples, advance_requested = "zero_pose", [], False + fig.canvas.manager.set_window_title(f"[{joint_idx + 1}/{len(joint_list)}] {name} - ZERO POSE") + ax0.set_title(f"{name} - hold zero pose") + fig.canvas.draw() + bg0, bg1 = fig.canvas.copy_from_bbox(ax0.bbox), fig.canvas.copy_from_bbox(ax1.bbox) + logger.info(f"Step 2: Hold {name} in zero position, then press 'n'") + + elif phase == "ellipse" and advance_requested and not state["have_transform"]: + logger.info(" (Need valid fit first - keep moving the joint)") + advance_requested = False + + elif phase == "zero_pose" and advance_requested: + if len(zero_samples) >= params.sample_count: + zero_offset = float(np.mean(zero_samples[-params.sample_count :])) + joints_out[-1]["zero_offset"] = zero_offset + logger.info(f" -> {name} zero: {zero_offset:+.3f} rad ({np.degrees(zero_offset):+.1f}°)") + joint_idx += 1 + advance_requested = False + + if joint_idx >= len(joint_list): + # All joints done + calib = ExoskeletonCalibration( + version=2, + side=side, + adc_max=2**12 - 1, + joints=[ + ExoskeletonJointCalibration( + name=j["name"], + center_fit=j["center_fit"], + T=j["T"], + zero_offset=j.get("zero_offset", 0.0), + ) + for j in joints_out + ], + ) + save_path.parent.mkdir(parents=True, exist_ok=True) + with open(save_path, "w") as f: + json.dump(calib.to_dict(), f, indent=2) + logger.info(f"Saved calibration to {save_path}") + logger.info("Calibration complete!") + plt.close(fig) + return calib + + # Next joint + phase, state = "ellipse", reset_state() + name, pair = joint_list[joint_idx] + fig.canvas.manager.set_window_title( + f"[{joint_idx + 1}/{len(joint_list)}] {name} - ELLIPSE" + ) + ax0.set_title(f"{name} raw (filtered)") + fig.canvas.draw() + bg0, bg1 = fig.canvas.copy_from_bbox(ax0.bbox), fig.canvas.copy_from_bbox(ax1.bbox) + logger.info(f"[{joint_idx + 1}/{len(joint_list)}] Calibrating {name}") + logger.info("Step 1: Move joint around to map ellipse, then press 'n'") + else: + logger.info( + f" (Collecting samples: {len(zero_samples)}/{params.sample_count} - hold still)" + ) + advance_requested = False + + # Read sensor + raw16 = read_raw_from_serial(ser) + if raw16 is not None: + x_raw, y_raw, s_raw, c_raw = read_joint_point(raw16, pair) + + if phase == "ellipse": + if state["have_transform"]: + z = state["T"] @ (np.array([x_raw, y_raw]) - state["center_fit"]) + state["xu"].append(float(z[0])) + state["yu"].append(float(z[1])) + state["latest_z"] = (float(z[0]), float(z[1])) + state["win_s"].append(s_raw) + state["win_c"].append(c_raw) + if len(state["win_s"]) >= max(3, params.median_window): + state["ys"].append(running_median(state["win_s"]) - (2**12 - 1) / 2) + state["xs"].append(running_median(state["win_c"]) - (2**12 - 1) / 2) + else: + jdata = joints_out[-1] + z = np.array(jdata["T"]) @ (np.array([x_raw, y_raw]) - np.array(jdata["center_fit"])) + zero_samples.append(float(np.arctan2(z[1], z[0]))) + state["latest_z"] = (float(z[0]), float(z[1])) + + # Ellipse fitting + t = time.time() + if ( + phase == "ellipse" + and (t - state["last_fit"]) >= params.fit_every + and len(state["xs"]) >= params.min_fit_points + ): + xfit, yfit = select_fit_subset(state["xs"], state["ys"]) + if xfit is not None and len(xfit) >= params.min_fit_points: + fit = fit_ellipse_opencv(xfit, yfit) + if fit is not None: + state["center_fit"] = fit["center"] + state["T"] = np.diag([1.0 / fit["a"], 1.0 / fit["b"]]) @ fit["R"].T + state["ellipse_cache"] = (fit["ex"], fit["ey"]) + state["have_transform"] = True + state["last_fit"] = t + + # Drawing + if (t - last_draw) >= 1.0 / params.draw_hz: + fig.canvas.restore_region(bg0) + fig.canvas.restore_region(bg1) + + if phase == "ellipse": + sc0.set_offsets(np.c_[state["xs"], state["ys"]] if state["xs"] else np.empty((0, 2))) + ax0.draw_artist(sc0) + ell_line.set_data(*state["ellipse_cache"] if state["ellipse_cache"] else ([], [])) + ax0.draw_artist(ell_line) + sc1.set_offsets(np.c_[state["xu"], state["yu"]] if state["xu"] else np.empty((0, 2))) + ax1.draw_artist(sc1) + if state["latest_z"]: + zx, zy = state["latest_z"] + radius_line.set_data([0.0, zx], [0.0, zy]) + ang = float(np.arctan2(zy, zx)) + angle_text.set_text( + f"angle: {ang:+.3f} rad ({np.degrees(ang):+.1f}°)\nmove {name}, press 'n' to advance" + ) + else: + radius_line.set_data([], []) + angle_text.set_text("(waiting for fit)") + else: + sc0.set_offsets(np.empty((0, 2))) + ax0.draw_artist(sc0) + ell_line.set_data([], []) + ax0.draw_artist(ell_line) + if state["latest_z"]: + zx, zy = state["latest_z"] + sc1.set_offsets([[zx, zy]]) + radius_line.set_data([0.0, zx], [0.0, zy]) + ang = float(np.arctan2(zy, zx)) + angle_text.set_text( + f"Zero pose for {name}\nangle: {ang:+.3f} rad\nsamples: {len(zero_samples)}/{params.sample_count}\nhold still, press 'n'" + ) + else: + sc1.set_offsets(np.empty((0, 2))) + radius_line.set_data([], []) + angle_text.set_text("(waiting for data)") + ax1.draw_artist(sc1) + + ax1.draw_artist(radius_line) + ax1.draw_artist(angle_text) + fig.canvas.blit(ax0.bbox) + fig.canvas.blit(ax1.bbox) + fig.canvas.flush_events() + last_draw = t + + plt.pause(0.001) + + finally: + plt.close(fig) diff --git a/src/lerobot/teleoperators/unitree_g1/exo_ik.py b/src/lerobot/teleoperators/unitree_g1/exo_ik.py new file mode 100644 index 000000000..92519540f --- /dev/null +++ b/src/lerobot/teleoperators/unitree_g1/exo_ik.py @@ -0,0 +1,353 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +IK helper for exoskeleton-to-G1 teleoperation. We map Exoskeleton joint angles to end-effector pose in world frame, +visualizing the result in meshcat after calibration. +""" + +import logging +import os +from dataclasses import dataclass + +import numpy as np + +from lerobot.robots.unitree_g1.g1_utils import G1_29_JointArmIndex +from lerobot.robots.unitree_g1.robot_kinematic_processor import G1_29_ArmIK + +from .exo_calib import JOINTS + +logger = logging.getLogger(__name__) + + +def _frame_id(model, name: str) -> int | None: + try: + fid = model.getFrameId(name) + return fid if 0 <= fid < model.nframes else None + except Exception: + return None + + +@dataclass +class ArmCfg: + side: str # "left" | "right" + urdf: str # exo_left.urdf / exo_right.urdf + root: str # "exo_left" / "exo_right" + g1_ee: str # "l_ee" / "r_ee" + offset: np.ndarray # world offset for viz + target + marker_prefix: str # "left" / "right" + + +class Markers: + """Creates meshcat visualization primitives, showing end-effector frames of exoskeleton and G1""" + + def __init__(self, viewer): + self.v = viewer + + def sphere(self, path: str, r: float, rgba: tuple[float, float, float, float]): + import meshcat.geometry as mg + + c = (int(rgba[0] * 255) << 16) | (int(rgba[1] * 255) << 8) | int(rgba[2] * 255) + self.v[path].set_object( + mg.Sphere(r), + mg.MeshPhongMaterial(color=c, opacity=rgba[3], transparent=rgba[3] < 1.0), + ) + + def axes(self, path: str, axis_len: float = 0.1, axis_w: int = 6): + import meshcat.geometry as mg + + pts = np.array( + [[0, 0, 0], [axis_len, 0, 0], [0, 0, 0], [0, axis_len, 0], [0, 0, 0], [0, 0, axis_len]], + dtype=np.float32, + ).T + cols = np.array( + [[1, 0, 0], [1, 0, 0], [0, 1, 0], [0, 1, 0], [0, 0, 1], [0, 0, 1]], + dtype=np.float32, + ).T + self.v[path].set_object( + mg.LineSegments( + mg.PointsGeometry(position=pts, color=cols), + mg.LineBasicMaterial(linewidth=axis_w, vertexColors=True), + ) + ) + + def tf(self, path: str, mat: np.ndarray): + self.v[path].set_transform(mat) + + +class ExoskeletonIKHelper: + """ + - Loads G1 robot and exoskeleton URDF models via Pinocchio + - Computes forward kinematics on exoskeleton to get end-effector poses + - Solves inverse kinematics on G1 to match those poses + - Provides meshcat visualization showing both robots and targets + + Args: + frozen_joints: List of G1 joint names to exclude from IK (kept at neutral). + """ + + def __init__(self, frozen_joints: list[str] | None = None): + try: + import pinocchio as pin + except ImportError as e: + raise ImportError("ik mode needs pinocchio: pip install pin") from e + + self.pin = pin + self.frozen_joints = frozen_joints or [] + + self.g1_ik = G1_29_ArmIK() + self.robot_g1 = self.g1_ik.reduced_robot + self.robot_g1.data = self.robot_g1.model.createData() + self.q_g1 = pin.neutral(self.robot_g1.model) + + assets_dir = os.path.join(self.g1_ik.repo_path, "assets") + + self.frozen_idx = self._frozen_joint_indices() + + self.arms = [ + ArmCfg( + side="left", + urdf=os.path.join(assets_dir, "exo_left.urdf"), + root="exo_left", + g1_ee="L_ee", + offset=np.array([0.6, 0.3, 0.0]), + marker_prefix="left", + ), + ArmCfg( + side="right", + urdf=os.path.join(assets_dir, "exo_right.urdf"), + root="exo_right", + g1_ee="R_ee", + offset=np.array([0.6, -0.3, 0.0]), + marker_prefix="right", + ), + ] + + self.exo = {} # side -> pin.RobotWrapper + self.q_exo = {} # side -> q + self.ee_id_exo = {} # side -> frame id + self.qmap = {} # side -> {joint_name: q_idx} + self.ee_id_g1 = {} # side -> frame id + + self._load_exo_models(assets_dir) + for a in self.arms: + self.ee_id_g1[a.side] = _frame_id(self.robot_g1.model, a.g1_ee) + + self.viewer = None + self.markers: Markers | None = None + self.viz_g1 = None + self.viz_exo = {} # side -> viz + + def _frozen_joint_indices(self) -> dict[str, int]: + out = {} + m = self.robot_g1.model + for name in self.frozen_joints: + if name in m.names: + jid = m.getJointId(name) + out[name] = m.idx_qs[jid] + logger.info(f"freezing joint: {name} (q_idx={out[name]})") + return out + + def _find_exo_ee(self, model, ee_name: str = "ee") -> int: + ee = _frame_id(model, ee_name) + if ee is not None: + return ee + for fid in reversed(range(model.nframes)): + if model.frames[fid].type == self.pin.FrameType.BODY: + return fid + return 0 + + def _build_joint_map(self, robot) -> dict[str, int]: + m = robot.model + return {n: m.idx_qs[m.getJointId(n)] for n in JOINTS if n in m.names} + + def _load_exo_models(self, assets_dir: str): + pin = self.pin + for a in self.arms: + if not os.path.exists(a.urdf): + logger.warning(f"{a.side} exo urdf not found: {a.urdf}") + continue + r = pin.RobotWrapper.BuildFromURDF(a.urdf, assets_dir) + self.exo[a.side] = r + self.q_exo[a.side] = pin.neutral(r.model) + self.ee_id_exo[a.side] = self._find_exo_ee(r.model) + self.qmap[a.side] = self._build_joint_map(r) + logger.info(f"loaded {a.side} exo urdf: {a.urdf}") + + def init_visualization(self): + """ + Creates a browser-based visualization of exoskeleton and G1 robot, + highlighting end-effector frames and target positions. + """ + try: + from pinocchio.visualize import MeshcatVisualizer + except ImportError as e: + logger.warning(f"meshcat viz unavailable: {e}") + return + + # g1 + self.viz_g1 = MeshcatVisualizer( + self.robot_g1.model, self.robot_g1.collision_model, self.robot_g1.visual_model + ) + self.viz_g1.initViewer(open=True) + self.viz_g1.loadViewerModel("g1") + self.viz_g1.display(self.q_g1) + + self.viewer = self.viz_g1.viewer + self.markers = Markers(self.viewer) + + # exos + for a in self.arms: + if a.side not in self.exo: + continue + r = self.exo[a.side] + v = MeshcatVisualizer(r.model, r.collision_model, r.visual_model) + v.initViewer(open=False) + v.viewer = self.viewer + v.loadViewerModel(a.root) + offset_tf = np.eye(4) + offset_tf[:3, 3] = a.offset + self.viewer[a.root].set_transform(offset_tf) + v.display(self.q_exo[a.side]) + self.viz_exo[a.side] = v + + # markers + for a in self.arms: + p = a.marker_prefix + self.markers.sphere(f"markers/{p}_exo_ee", 0.012, (0.2, 1.0, 0.2, 0.9)) + self.markers.sphere(f"markers/{p}_g1_ee", 0.015, (1.0, 0.2, 0.2, 0.9)) + self.markers.sphere(f"markers/{p}_ik_target", 0.015, (0.1, 0.3, 1.0, 0.9)) + self.markers.axes(f"markers/{p}_exo_axes", 0.06) + self.markers.axes(f"markers/{p}_g1_axes", 0.08) + + logger.info(f"meshcat viz initialized: {self.viewer.url()}") + print(f"\nmeshcat url: {self.viewer.url()}\n") + + def _fk_target_world(self, side: str, angles: dict[str, float]) -> np.ndarray | None: + """returns wrist frame target to be used for G1 IK in 4x4 homogeneous transform. Takes offset into account.""" + if side not in self.exo or not angles: + return None + + pin = self.pin + q = self.q_exo[side] + qmap = self.qmap[side] + + for name, ang in angles.items(): + idx = qmap.get(name) + if idx is not None: + q[idx] = float(ang) + + r = self.exo[side] + pin.forwardKinematics(r.model, r.data, q) + pin.updateFramePlacements(r.model, r.data) + + ee = r.data.oMf[self.ee_id_exo[side]] + target = np.eye(4) + target[:3, :3] = ee.rotation + # offset gets applied in world space + cfg = next(a for a in self.arms if a.side == side) + target[:3, 3] = cfg.offset + ee.translation + return target + + def update_visualization(self): + if self.viewer is None or self.markers is None: + return + + pin = self.pin + + # g1 + if self.viz_g1 is not None: + self.viz_g1.display(self.q_g1) + pin.forwardKinematics(self.robot_g1.model, self.robot_g1.data, self.q_g1) + pin.updateFramePlacements(self.robot_g1.model, self.robot_g1.data) + + for a in self.arms: + fid = self.ee_id_g1.get(a.side) + if fid is None: + continue + ee_tf = self.robot_g1.data.oMf[fid].homogeneous + p = a.marker_prefix + self.markers.tf(f"markers/{p}_g1_ee", ee_tf) + self.markers.tf(f"markers/{p}_g1_axes", ee_tf) + + # exos + for a in self.arms: + side = a.side + v = self.viz_exo.get(side) + if v is None: + continue + + v.display(self.q_exo[side]) + r = self.exo[side] + pin.forwardKinematics(r.model, r.data, self.q_exo[side]) + pin.updateFramePlacements(r.model, r.data) + + ee = r.data.oMf[self.ee_id_exo[side]] + world_tf = (pin.SE3(np.eye(3), a.offset) * ee).homogeneous + p = a.marker_prefix + self.markers.tf(f"markers/{p}_exo_ee", world_tf) + self.markers.tf(f"markers/{p}_exo_axes", world_tf) + + target_tf = np.eye(4) + target_tf[:3, :3] = ee.rotation + target_tf[:3, 3] = a.offset + ee.translation + self.markers.tf(f"markers/{p}_ik_target", target_tf) + + def compute_g1_joints_from_exo( + self, + left_angles: dict[str, float], + right_angles: dict[str, float], + ) -> dict[str, float]: + """ + Performs FK on exoskeleton to get end-effector poses in world frame, + after which it solves IK on G1 to return joint angles matching those poses in G1 motor order. + """ + pin = self.pin + + targets = { + "left": self._fk_target_world("left", left_angles), + "right": self._fk_target_world("right", right_angles), + } + + # fallback to current g1 ee pose if missing target + pin.forwardKinematics(self.robot_g1.model, self.robot_g1.data, self.q_g1) + pin.updateFramePlacements(self.robot_g1.model, self.robot_g1.data) + + for a in self.arms: + if targets[a.side] is not None: + continue + fid = self.ee_id_g1.get(a.side) + if fid is not None: + targets[a.side] = self.robot_g1.data.oMf[fid].homogeneous + + if targets["left"] is None or targets["right"] is None: + logger.warning("missing ik targets, returning current pose") + return {} + + frozen_vals = {n: self.q_g1[i] for n, i in self.frozen_idx.items()} + + self.q_g1, _ = self.g1_ik.solve_ik( + targets["left"], targets["right"], current_lr_arm_motor_q=self.q_g1 + ) + + for n, i in self.frozen_idx.items(): + self.q_g1[i] = frozen_vals[n] + + return { + f"{j.name}.q": float(self.q_g1[i]) + for i, j in enumerate(G1_29_JointArmIndex) + if i < len(self.q_g1) + } diff --git a/src/lerobot/teleoperators/unitree_g1/exo_serial.py b/src/lerobot/teleoperators/unitree_g1/exo_serial.py new file mode 100644 index 000000000..1211c57cc --- /dev/null +++ b/src/lerobot/teleoperators/unitree_g1/exo_serial.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import json +import logging +from dataclasses import dataclass +from pathlib import Path + +import serial + +from .exo_calib import ExoskeletonCalibration, exo_raw_to_angles, run_exo_calibration + +logger = logging.getLogger(__name__) + + +def parse_raw16(line: bytes) -> list[int] | None: + try: + parts = line.decode("utf-8", errors="ignore").split() + if len(parts) < 16: + return None + return [int(x) for x in parts[:16]] + except Exception: + return None + + +def read_raw_from_serial(ser) -> list[int] | None: + """Read latest sample from serial; if buffer is backed up, keep only the newest.""" + last = None + while ser.in_waiting > 0: + b = ser.readline() + if not b: + break + raw16 = parse_raw16(b) + if raw16 is not None: + last = raw16 + if last is None: + b = ser.readline() + if b: + last = parse_raw16(b) + return last + + +@dataclass +class ExoskeletonArm: + port: str + calibration_fpath: Path + side: str + baud_rate: int = 115200 + + _ser: serial.Serial | None = None + calibration: ExoskeletonCalibration | None = None + + def __post_init__(self): + if self.calibration_fpath.is_file(): + self._load_calibration() + + @property + def is_connected(self) -> bool: + return self._ser is not None and getattr(self._ser, "is_open", False) + + @property + def is_calibrated(self) -> bool: + return self.calibration is not None + + def connect(self, calibrate: bool = True) -> None: + if self.is_connected: + return + try: + self._ser = serial.Serial(self.port, self.baud_rate, timeout=0.02) + self._ser.reset_input_buffer() + logger.info(f"connected: {self.port}") + except serial.SerialException as e: + raise ConnectionError(f"failed to connect to {self.port}: {e}") from e + + if calibrate and not self.is_calibrated: + self.calibrate() + + def disconnect(self) -> None: + if self._ser: + try: + self._ser.close() + finally: + self._ser = None + + def _load_calibration(self) -> None: + try: + data = json.loads(self.calibration_fpath.read_text()) + self.calibration = ExoskeletonCalibration.from_dict(data) + logger.info(f"loaded calibration: {self.calibration_fpath}") + except Exception as e: + logger.warning(f"failed to load calibration: {e}") + + def read_raw(self) -> list[int] | None: + if not self._ser: + return None + return read_raw_from_serial(self._ser) + + def get_angles(self) -> dict[str, float]: + if not self.calibration: + raise RuntimeError("exoskeleton not calibrated") + raw = self.read_raw() + return {} if raw is None else exo_raw_to_angles(raw, self.calibration) + + def calibrate(self) -> None: + ser = self._ser + self.calibration = run_exo_calibration(ser, self.side, self.calibration_fpath) diff --git a/src/lerobot/teleoperators/unitree_g1/unitree_g1.py b/src/lerobot/teleoperators/unitree_g1/unitree_g1.py new file mode 100644 index 000000000..3779d83ec --- /dev/null +++ b/src/lerobot/teleoperators/unitree_g1/unitree_g1.py @@ -0,0 +1,157 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import time +from functools import cached_property + +from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex +from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, TELEOPERATORS + +from ..teleoperator import Teleoperator +from .config_unitree_g1 import UnitreeG1TeleoperatorConfig +from .exo_ik import ExoskeletonIKHelper +from .exo_serial import ExoskeletonArm + +logger = logging.getLogger(__name__) + + +class UnitreeG1Teleoperator(Teleoperator): + """ + Bimanual exoskeleton arms teleoperator for Unitree G1 arms. + + Uses inverse kinematics: exoskeleton FK computes end-effector pose, + G1 IK solves for joint angles. + """ + + config_class = UnitreeG1TeleoperatorConfig + name = "unitree_g1" + + def __init__(self, config: UnitreeG1TeleoperatorConfig): + super().__init__(config) + self.config = config + + # Setup calibration directory + self.calibration_dir = ( + config.calibration_dir + if config.calibration_dir + else HF_LEROBOT_CALIBRATION / TELEOPERATORS / self.name + ) + self.calibration_dir.mkdir(parents=True, exist_ok=True) + + left_id = f"{config.id}_left" if config.id else "left" + right_id = f"{config.id}_right" if config.id else "right" + + # Create exoskeleton arm instances + self.left_arm = ExoskeletonArm( + port=config.left_arm_config.port, + baud_rate=config.left_arm_config.baud_rate, + calibration_fpath=self.calibration_dir / f"{left_id}.json", + side="left", + ) + self.right_arm = ExoskeletonArm( + port=config.right_arm_config.port, + baud_rate=config.right_arm_config.baud_rate, + calibration_fpath=self.calibration_dir / f"{right_id}.json", + side="right", + ) + + self.ik_helper: ExoskeletonIKHelper | None = None + + @cached_property + def action_features(self) -> dict[str, type]: + return {f"{name}.q": float for name in self._g1_joint_names} + + @cached_property + def feedback_features(self) -> dict[str, type]: + return {} + + @property + def is_connected(self) -> bool: + return self.left_arm.is_connected and self.right_arm.is_connected + + @property + def is_calibrated(self) -> bool: + return self.left_arm.is_calibrated and self.right_arm.is_calibrated + + def connect(self, calibrate: bool = True) -> None: + self.left_arm.connect(calibrate) + self.right_arm.connect(calibrate) + + frozen_joints = [j.strip() for j in self.config.frozen_joints.split(",") if j.strip()] + self.ik_helper = ExoskeletonIKHelper(frozen_joints=frozen_joints) + logger.info("IK helper initialized") + + def calibrate(self) -> None: + if not self.left_arm.is_calibrated: + logger.info("Starting calibration for left arm...") + self.left_arm.calibrate() + else: + logger.info("Left arm already calibrated. Skipping.") + + if not self.right_arm.is_calibrated: + logger.info("Starting calibration for right arm...") + self.right_arm.calibrate() + else: + logger.info("Right arm already calibrated. Skipping.") + + logger.info("Starting visualization to verify calibration...") + self.run_visualization_loop() + + def configure(self) -> None: + pass + + def get_action(self) -> dict[str, float]: + left_angles = self.left_arm.get_angles() + right_angles = self.right_arm.get_angles() + return self.ik_helper.compute_g1_joints_from_exo(left_angles, right_angles) + + def send_feedback(self, feedback: dict[str, float]) -> None: + raise NotImplementedError("Exoskeleton arms do not support feedback") + + def disconnect(self) -> None: + self.left_arm.disconnect() + self.right_arm.disconnect() + + def run_visualization_loop(self): + """Run interactive Meshcat visualization loop to verify tracking.""" + if self.ik_helper is None: + frozen_joints = [j.strip() for j in self.config.frozen_joints.split(",") if j.strip()] + self.ik_helper = ExoskeletonIKHelper(frozen_joints=frozen_joints) + + self.ik_helper.init_visualization() + + print("\n" + "=" * 60) + print("Visualization running! Move the exoskeletons to test tracking.") + print("Press Ctrl+C to exit.") + print("=" * 60 + "\n") + + try: + while True: + left_angles = self.left_arm.get_angles() + right_angles = self.right_arm.get_angles() + + self.ik_helper.compute_g1_joints_from_exo(left_angles, right_angles) + self.ik_helper.update_visualization() + + time.sleep(0.01) + + except KeyboardInterrupt: + print("\n\nVisualization stopped.") + + @cached_property + def _g1_joint_names(self) -> list[str]: + return [joint.name for joint in G1_29_JointIndex] diff --git a/src/lerobot/teleoperators/utils.py b/src/lerobot/teleoperators/utils.py index eec2f119c..16454d5ad 100644 --- a/src/lerobot/teleoperators/utils.py +++ b/src/lerobot/teleoperators/utils.py @@ -13,12 +13,14 @@ # limitations under the License. from enum import Enum -from typing import cast +from typing import TYPE_CHECKING, cast from lerobot.utils.import_utils import make_device_from_device_class from .config import TeleoperatorConfig -from .teleoperator import Teleoperator + +if TYPE_CHECKING: + from .teleoperator import Teleoperator class TeleopEvents(Enum): @@ -31,7 +33,7 @@ class TeleopEvents(Enum): TERMINATE_EPISODE = "terminate_episode" -def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator: +def make_teleoperator_from_config(config: TeleoperatorConfig) -> "Teleoperator": # TODO(Steven): Consider just using the make_device_from_device_class for all types if config.type == "keyboard": from .keyboard import KeyboardTeleop @@ -73,6 +75,10 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator: from .homunculus import HomunculusArm return HomunculusArm(config) + elif config.type == "unitree_g1": + from .unitree_g1 import UnitreeG1Teleoperator + + return UnitreeG1Teleoperator(config) elif config.type == "bi_so_leader": from .bi_so_leader import BiSOLeader @@ -81,8 +87,16 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> Teleoperator: from .reachy2_teleoperator import Reachy2Teleoperator return Reachy2Teleoperator(config) + elif config.type == "openarm_leader": + from .openarm_leader import OpenArmLeader + + return OpenArmLeader(config) + elif config.type == "bi_openarm_leader": + from .bi_openarm_leader import BiOpenArmLeader + + return BiOpenArmLeader(config) else: try: - return cast(Teleoperator, make_device_from_device_class(config)) + return cast("Teleoperator", make_device_from_device_class(config)) except Exception as e: raise ValueError(f"Error creating robot with config {config}: {e}") from e diff --git a/src/lerobot/utils/constants.py b/src/lerobot/utils/constants.py index 09ab250f3..577a18a9e 100644 --- a/src/lerobot/utils/constants.py +++ b/src/lerobot/utils/constants.py @@ -31,7 +31,8 @@ OBS_LANGUAGE_USER_PROMPT_TOKENS = OBS_LANGUAGE_USER_PROMPT + ".tokens" OBS_LANGUAGE_USER_PROMPT_ATTENTION_MASK = OBS_LANGUAGE_USER_PROMPT_TOKENS + ".attention_mask" OBS_LANGUAGE_SUBTASK = OBS_STR + ".subtask" OBS_LANGUAGE_SUBTASK_TOKENS = OBS_LANGUAGE_SUBTASK + ".tokens" -OBS_LANGUAGE_SUBTASK_ATTENTION_MASK = OBS_LANGUAGE_SUBTASK_TOKENS + ".attention_mask" +OBS_LANGUAGE_SUBTASK_ATTENTION_MASK = OBS_LANGUAGE_SUBTASK + ".attention_mask" + ACTION = "action" ACTION_PREFIX = ACTION + "." ACTION_TOKENS = ACTION + ".tokens" diff --git a/tests/cameras/test_opencv.py b/tests/cameras/test_opencv.py index 3cf3793b6..feb700631 100644 --- a/tests/cameras/test_opencv.py +++ b/tests/cameras/test_opencv.py @@ -20,7 +20,9 @@ # ``` from pathlib import Path +from unittest.mock import patch +import cv2 import numpy as np import pytest @@ -28,6 +30,50 @@ from lerobot.cameras.configs import Cv2Rotation from lerobot.cameras.opencv import OpenCVCamera, OpenCVCameraConfig from lerobot.utils.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError +RealVideoCapture = cv2.VideoCapture + + +class MockLoopingVideoCapture: + """ + Wraps the real OpenCV VideoCapture. + Motivation: cv2.VideoCapture(file.png) is only valid for one read. + Strategy: Read the file once & return the cached frame for subsequent reads. + Consequence: No recurrent I/O operations, but we keep the test artifacts simple. + """ + + def __init__(self, *args, **kwargs): + args_clean = [str(a) if isinstance(a, Path) else a for a in args] + self._real_vc = RealVideoCapture(*args_clean, **kwargs) + self._cached_frame = None + + def read(self): + ret, frame = self._real_vc.read() + + if ret: + self._cached_frame = frame + return ret, frame + + if not ret and self._cached_frame is not None: + return True, self._cached_frame.copy() + + return ret, frame + + def __getattr__(self, name): + return getattr(self._real_vc, name) + + +@pytest.fixture(autouse=True) +def patch_opencv_videocapture(): + """ + Automatically patches cv2.VideoCapture for all tests. + """ + module_path = OpenCVCamera.__module__ + target = f"{module_path}.cv2.VideoCapture" + + with patch(target, new=MockLoopingVideoCapture): + yield + + # NOTE(Steven): more tests + assertions? TEST_ARTIFACTS_DIR = Path(__file__).parent.parent / "artifacts" / "cameras" DEFAULT_PNG_FILE_PATH = TEST_ARTIFACTS_DIR / "image_160x120.png" @@ -43,25 +89,22 @@ def test_abc_implementation(): def test_connect(): - config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH) - camera = OpenCVCamera(config) + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0) - camera.connect(warmup=False) - - assert camera.is_connected + with OpenCVCamera(config) as camera: + assert camera.is_connected def test_connect_already_connected(): - config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH) - camera = OpenCVCamera(config) - camera.connect(warmup=False) + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0) - with pytest.raises(DeviceAlreadyConnectedError): - camera.connect(warmup=False) + with OpenCVCamera(config) as camera, pytest.raises(DeviceAlreadyConnectedError): + camera.connect() def test_connect_invalid_camera_path(): config = OpenCVCameraConfig(index_or_path="nonexistent/camera.png") + camera = OpenCVCamera(config) with pytest.raises(ConnectionError): @@ -74,27 +117,25 @@ def test_invalid_width_connect(): width=99999, # Invalid width to trigger error height=480, ) - camera = OpenCVCamera(config) + camera = OpenCVCamera(config) with pytest.raises(RuntimeError): camera.connect(warmup=False) @pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES) def test_read(index_or_path): - config = OpenCVCameraConfig(index_or_path=index_or_path) - camera = OpenCVCamera(config) - camera.connect(warmup=False) + config = OpenCVCameraConfig(index_or_path=index_or_path, warmup_s=0) - img = camera.read() - - assert isinstance(img, np.ndarray) + with OpenCVCamera(config) as camera: + img = camera.read() + assert isinstance(img, np.ndarray) def test_read_before_connect(): config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH) - camera = OpenCVCamera(config) + camera = OpenCVCamera(config) with pytest.raises(DeviceNotConnectedError): _ = camera.read() @@ -119,32 +160,22 @@ def test_disconnect_before_connect(): @pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES) def test_async_read(index_or_path): - config = OpenCVCameraConfig(index_or_path=index_or_path) - camera = OpenCVCamera(config) - camera.connect(warmup=False) + config = OpenCVCameraConfig(index_or_path=index_or_path, warmup_s=0) - try: + with OpenCVCamera(config) as camera: img = camera.async_read() assert camera.thread is not None assert camera.thread.is_alive() assert isinstance(img, np.ndarray) - finally: - if camera.is_connected: - camera.disconnect() # To stop/join the thread. Otherwise get warnings when the test ends def test_async_read_timeout(): - config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH) - camera = OpenCVCamera(config) - camera.connect(warmup=False) + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0) - try: - with pytest.raises(TimeoutError): - camera.async_read(timeout_ms=0) - finally: - if camera.is_connected: - camera.disconnect() + with OpenCVCamera(config) as camera, pytest.raises(TimeoutError): + camera.async_read(timeout_ms=0) # consumes any available frame by then + camera.async_read(timeout_ms=0) # request immediately another one def test_async_read_before_connect(): @@ -155,6 +186,50 @@ def test_async_read_before_connect(): _ = camera.async_read() +def test_read_latest(): + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0) + + with OpenCVCamera(config) as camera: + # ensure at least one fresh frame is captured + frame = camera.read() + latest = camera.read_latest() + + assert isinstance(latest, np.ndarray) + assert latest.shape == frame.shape + + +def test_read_latest_before_connect(): + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH) + + camera = OpenCVCamera(config) + with pytest.raises(DeviceNotConnectedError): + _ = camera.read_latest() + + +def test_read_latest_high_frequency(): + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0) + + with OpenCVCamera(config) as camera: + # prime to ensure frames are available + ref = camera.read() + + for _ in range(20): + latest = camera.read_latest() + assert isinstance(latest, np.ndarray) + assert latest.shape == ref.shape + + +def test_read_latest_too_old(): + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, warmup_s=0) + + with OpenCVCamera(config) as camera: + # prime to ensure frames are available + _ = camera.read() + + with pytest.raises(TimeoutError): + _ = camera.read_latest(max_age_ms=0) # immediately too old + + def test_fourcc_configuration(): """Test FourCC configuration validation and application.""" @@ -181,18 +256,15 @@ def test_fourcc_configuration(): def test_fourcc_with_camera(): """Test FourCC functionality with actual camera connection.""" - config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, fourcc="MJPG") - camera = OpenCVCamera(config) + config = OpenCVCameraConfig(index_or_path=DEFAULT_PNG_FILE_PATH, fourcc="MJPG", warmup_s=0) # Connect should work with MJPG specified - camera.connect(warmup=False) - assert camera.is_connected + with OpenCVCamera(config) as camera: + assert camera.is_connected - # Read should work normally - img = camera.read() - assert isinstance(img, np.ndarray) - - camera.disconnect() + # Read should work normally + img = camera.read() + assert isinstance(img, np.ndarray) @pytest.mark.parametrize("index_or_path", TEST_IMAGE_PATHS, ids=TEST_IMAGE_SIZES) @@ -211,18 +283,16 @@ def test_rotation(rotation, index_or_path): dimensions = filename.split("_")[-1].split(".")[0] # Assumes filenames format (_wxh.png) original_width, original_height = map(int, dimensions.split("x")) - config = OpenCVCameraConfig(index_or_path=index_or_path, rotation=rotation) - camera = OpenCVCamera(config) - camera.connect(warmup=False) + config = OpenCVCameraConfig(index_or_path=index_or_path, rotation=rotation, warmup_s=0) + with OpenCVCamera(config) as camera: + img = camera.read() + assert isinstance(img, np.ndarray) - img = camera.read() - assert isinstance(img, np.ndarray) - - if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270): - assert camera.width == original_height - assert camera.height == original_width - assert img.shape[:2] == (original_width, original_height) - else: - assert camera.width == original_width - assert camera.height == original_height - assert img.shape[:2] == (original_height, original_width) + if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270): + assert camera.width == original_height + assert camera.height == original_width + assert img.shape[:2] == (original_width, original_height) + else: + assert camera.width == original_width + assert camera.height == original_height + assert img.shape[:2] == (original_height, original_width) diff --git a/tests/cameras/test_reachy2_camera.py b/tests/cameras/test_reachy2_camera.py index 14774bf38..2aebfdf0a 100644 --- a/tests/cameras/test_reachy2_camera.py +++ b/tests/cameras/test_reachy2_camera.py @@ -150,6 +150,44 @@ def test_async_read_before_connect(camera): _ = camera.async_read() +def test_read_latest(camera): + camera.connect() + + frame = camera.read() + latest = camera.read_latest() + + assert isinstance(latest, np.ndarray) + assert latest.shape == frame.shape + + +def test_read_latest_before_connect(camera): + # camera fixture yields an unconnected camera instance + with pytest.raises(DeviceNotConnectedError): + _ = camera.read_latest() + + +def test_read_latest_high_frequency(camera): + camera.connect() + + # prime to ensure frames are available + ref = camera.read() + + for _ in range(20): + latest = camera.read_latest() + assert isinstance(latest, np.ndarray) + assert latest.shape == ref.shape + + +def test_read_latest_too_old(camera): + camera.connect() + + # prime to ensure frames are available + _ = camera.read() + + with pytest.raises(TimeoutError): + _ = camera.read_latest(max_age_ms=0) # immediately too old + + def test_wrong_camera_name(): with pytest.raises(ValueError): _ = Reachy2CameraConfig(name="wrong-name", image_type="left") diff --git a/tests/cameras/test_realsense.py b/tests/cameras/test_realsense.py index fb9912257..1deb73f05 100644 --- a/tests/cameras/test_realsense.py +++ b/tests/cameras/test_realsense.py @@ -62,19 +62,15 @@ def test_abc_implementation(): def test_connect(): - config = RealSenseCameraConfig(serial_number_or_name="042") - camera = RealSenseCamera(config) + config = RealSenseCameraConfig(serial_number_or_name="042", warmup_s=0) - camera.connect(warmup=False) - assert camera.is_connected + with RealSenseCamera(config) as camera: + assert camera.is_connected def test_connect_already_connected(): - config = RealSenseCameraConfig(serial_number_or_name="042") - camera = RealSenseCamera(config) - camera.connect(warmup=False) - - with pytest.raises(DeviceAlreadyConnectedError): + config = RealSenseCameraConfig(serial_number_or_name="042", warmup_s=0) + with RealSenseCamera(config) as camera, pytest.raises(DeviceAlreadyConnectedError): camera.connect(warmup=False) @@ -96,12 +92,10 @@ def test_invalid_width_connect(): def test_read(): - config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30) - camera = RealSenseCamera(config) - camera.connect(warmup=False) - - img = camera.read() - assert isinstance(img, np.ndarray) + config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0) + with RealSenseCamera(config) as camera: + img = camera.read() + assert isinstance(img, np.ndarray) # TODO(Steven): Fix this test for the latest version of pyrealsense2. @@ -142,32 +136,21 @@ def test_disconnect_before_connect(): def test_async_read(): - config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30) - camera = RealSenseCamera(config) - camera.connect(warmup=False) + config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0) - try: + with RealSenseCamera(config) as camera: img = camera.async_read() assert camera.thread is not None assert camera.thread.is_alive() assert isinstance(img, np.ndarray) - finally: - if camera.is_connected: - camera.disconnect() # To stop/join the thread. Otherwise get warnings when the test ends def test_async_read_timeout(): - config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30) - camera = RealSenseCamera(config) - camera.connect(warmup=False) - - try: - with pytest.raises(TimeoutError): - camera.async_read(timeout_ms=0) - finally: - if camera.is_connected: - camera.disconnect() + config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0) + with RealSenseCamera(config) as camera, pytest.raises(TimeoutError): + camera.async_read(timeout_ms=0) # consumes any available frame by then + camera.async_read(timeout_ms=0) # request immediately another one def test_async_read_before_connect(): @@ -178,6 +161,47 @@ def test_async_read_before_connect(): _ = camera.async_read() +def test_read_latest(): + config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0) + with RealSenseCamera(config) as camera: + img = camera.read() + latest = camera.read_latest() + + assert isinstance(latest, np.ndarray) + assert latest.shape == img.shape + + +def test_read_latest_high_frequency(): + config = RealSenseCameraConfig(serial_number_or_name="042", width=640, height=480, fps=30, warmup_s=0) + with RealSenseCamera(config) as camera: + # prime with one read to ensure frames are available + ref = camera.read() + + for _ in range(20): + latest = camera.read_latest() + assert isinstance(latest, np.ndarray) + assert latest.shape == ref.shape + + +def test_read_latest_before_connect(): + config = RealSenseCameraConfig(serial_number_or_name="042") + camera = RealSenseCamera(config) + + with pytest.raises(DeviceNotConnectedError): + _ = camera.read_latest() + + +def test_read_latest_too_old(): + config = RealSenseCameraConfig(serial_number_or_name="042") + + with RealSenseCamera(config) as camera: + # prime to ensure frames are available + _ = camera.read() + + with pytest.raises(TimeoutError): + _ = camera.read_latest(max_age_ms=0) # immediately too old + + @pytest.mark.parametrize( "rotation", [ @@ -189,18 +213,16 @@ def test_async_read_before_connect(): ids=["no_rot", "rot90", "rot180", "rot270"], ) def test_rotation(rotation): - config = RealSenseCameraConfig(serial_number_or_name="042", rotation=rotation) - camera = RealSenseCamera(config) - camera.connect(warmup=False) + config = RealSenseCameraConfig(serial_number_or_name="042", rotation=rotation, warmup_s=0) + with RealSenseCamera(config) as camera: + img = camera.read() + assert isinstance(img, np.ndarray) - img = camera.read() - assert isinstance(img, np.ndarray) - - if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270): - assert camera.width == 480 - assert camera.height == 640 - assert img.shape[:2] == (640, 480) - else: - assert camera.width == 640 - assert camera.height == 480 - assert img.shape[:2] == (480, 640) + if rotation in (Cv2Rotation.ROTATE_90, Cv2Rotation.ROTATE_270): + assert camera.width == 480 + assert camera.height == 640 + assert img.shape[:2] == (640, 480) + else: + assert camera.width == 640 + assert camera.height == 480 + assert img.shape[:2] == (480, 640) diff --git a/tests/datasets/test_aggregate.py b/tests/datasets/test_aggregate.py index 031c29d60..3609bac24 100644 --- a/tests/datasets/test_aggregate.py +++ b/tests/datasets/test_aggregate.py @@ -525,3 +525,92 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory): assert img.shape[0] == 3, f"Image {image_key} should have 3 channels" assert_dataset_iteration_works(aggr_ds) + + +def test_aggregate_already_merged_dataset(tmp_path, lerobot_dataset_factory): + """Regression test for aggregating a dataset that is itself a result of a previous merge. + + This test reproduces the bug where merging datasets with multiple parquet files + (e.g., from a previous merge with file rotation) would cause FileNotFoundError + because metadata file indices were incorrectly preserved instead of being mapped + to their actual destination files. + + The fix adds src_to_dst tracking in aggregate_data() to correctly map source + file indices to destination file indices. + """ + # Step 1: Create datasets A and B + ds_a = lerobot_dataset_factory( + root=tmp_path / "ds_a", + repo_id=f"{DUMMY_REPO_ID}_a", + total_episodes=4, + total_frames=200, + ) + ds_b = lerobot_dataset_factory( + root=tmp_path / "ds_b", + repo_id=f"{DUMMY_REPO_ID}_b", + total_episodes=4, + total_frames=200, + ) + + # Step 2: Merge A+B into AB with small file size to force multiple files + aggregate_datasets( + repo_ids=[ds_a.repo_id, ds_b.repo_id], + roots=[ds_a.root, ds_b.root], + aggr_repo_id=f"{DUMMY_REPO_ID}_ab", + aggr_root=tmp_path / "ds_ab", + data_files_size_in_mb=0.01, # Force file rotation + ) + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "ds_ab") + ds_ab = LeRobotDataset(f"{DUMMY_REPO_ID}_ab", root=tmp_path / "ds_ab") + + # Verify AB has multiple data files (file rotation occurred) + ab_data_files = list((tmp_path / "ds_ab" / "data").rglob("*.parquet")) + assert len(ab_data_files) > 1, "First merge should create multiple parquet files" + + # Step 3: Create dataset C + ds_c = lerobot_dataset_factory( + root=tmp_path / "ds_c", + repo_id=f"{DUMMY_REPO_ID}_c", + total_episodes=2, + total_frames=100, + ) + + # Step 4: Merge AB+C into final - THIS IS WHERE THE BUG OCCURRED + aggregate_datasets( + repo_ids=[ds_ab.repo_id, ds_c.repo_id], + roots=[ds_ab.root, ds_c.root], + aggr_repo_id=f"{DUMMY_REPO_ID}_abc", + aggr_root=tmp_path / "ds_abc", + ) + + with ( + patch("lerobot.datasets.lerobot_dataset.get_safe_version") as mock_get_safe_version, + patch("lerobot.datasets.lerobot_dataset.snapshot_download") as mock_snapshot_download, + ): + mock_get_safe_version.return_value = "v3.0" + mock_snapshot_download.return_value = str(tmp_path / "ds_abc") + ds_abc = LeRobotDataset(f"{DUMMY_REPO_ID}_abc", root=tmp_path / "ds_abc") + + # Step 5: Verify all data files referenced in metadata actually exist + for ep_idx in range(ds_abc.num_episodes): + data_file_path = ds_abc.root / ds_abc.meta.get_data_file_path(ep_idx) + assert data_file_path.exists(), ( + f"Episode {ep_idx} references non-existent file: {data_file_path}\n" + "This indicates the src_to_dst mapping fix is not working correctly." + ) + + # Step 6: Verify we can iterate through the entire dataset without FileNotFoundError + expected_episodes = ds_a.num_episodes + ds_b.num_episodes + ds_c.num_episodes + expected_frames = ds_a.num_frames + ds_b.num_frames + ds_c.num_frames + + assert ds_abc.num_episodes == expected_episodes + assert ds_abc.num_frames == expected_frames + + # This would raise FileNotFoundError before the fix + assert_dataset_iteration_works(ds_abc) diff --git a/tests/datasets/test_dataset_tools.py b/tests/datasets/test_dataset_tools.py index 35a369de9..1de199630 100644 --- a/tests/datasets/test_dataset_tools.py +++ b/tests/datasets/test_dataset_tools.py @@ -26,6 +26,7 @@ from lerobot.datasets.dataset_tools import ( delete_episodes, merge_datasets, modify_features, + modify_tasks, remove_feature, split_dataset, ) @@ -1050,6 +1051,174 @@ def test_modify_features_preserves_file_structure(sample_dataset, tmp_path): assert "reward" in modified_dataset.meta.features +def test_modify_tasks_single_task_for_all(sample_dataset): + """Test setting a single task for all episodes.""" + new_task = "Pick up the cube and place it" + + modified_dataset = modify_tasks(sample_dataset, new_task=new_task) + + # Verify all episodes have the new task + assert len(modified_dataset.meta.tasks) == 1 + assert new_task in modified_dataset.meta.tasks.index + + # Verify task_index is 0 for all frames (only one task) + for i in range(len(modified_dataset)): + item = modified_dataset[i] + assert item["task_index"].item() == 0 + assert item["task"] == new_task + + +def test_modify_tasks_episode_specific(sample_dataset): + """Test setting different tasks for specific episodes.""" + episode_tasks = { + 0: "Task A", + 1: "Task B", + 2: "Task A", + 3: "Task C", + 4: "Task B", + } + + modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks) + + # Verify correct number of unique tasks + unique_tasks = set(episode_tasks.values()) + assert len(modified_dataset.meta.tasks) == len(unique_tasks) + + # Verify each episode has the correct task + for ep_idx, expected_task in episode_tasks.items(): + ep_data = modified_dataset.meta.episodes[ep_idx] + assert ep_data["tasks"][0] == expected_task + + +def test_modify_tasks_default_with_overrides(sample_dataset): + """Test setting a default task with specific overrides.""" + default_task = "Default task" + override_task = "Special task" + episode_tasks = {2: override_task, 4: override_task} + + modified_dataset = modify_tasks( + sample_dataset, + new_task=default_task, + episode_tasks=episode_tasks, + ) + + # Verify correct number of unique tasks + assert len(modified_dataset.meta.tasks) == 2 + assert default_task in modified_dataset.meta.tasks.index + assert override_task in modified_dataset.meta.tasks.index + + # Verify episodes have correct tasks + for ep_idx in range(5): + ep_data = modified_dataset.meta.episodes[ep_idx] + if ep_idx in episode_tasks: + assert ep_data["tasks"][0] == override_task + else: + assert ep_data["tasks"][0] == default_task + + +def test_modify_tasks_no_task_specified(sample_dataset): + """Test error when no task is specified.""" + with pytest.raises(ValueError, match="Must specify at least one of new_task or episode_tasks"): + modify_tasks(sample_dataset) + + +def test_modify_tasks_invalid_episode_indices(sample_dataset): + """Test error with invalid episode indices.""" + with pytest.raises(ValueError, match="Invalid episode indices"): + modify_tasks(sample_dataset, episode_tasks={10: "Task", 20: "Task"}) + + +def test_modify_tasks_updates_info_json(sample_dataset): + """Test that total_tasks is updated in info.json.""" + episode_tasks = {0: "Task A", 1: "Task B", 2: "Task C", 3: "Task A", 4: "Task B"} + + modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks) + + # Verify total_tasks is updated + assert modified_dataset.meta.total_tasks == 3 + + +def test_modify_tasks_preserves_other_metadata(sample_dataset): + """Test that modifying tasks preserves other metadata.""" + original_frames = sample_dataset.meta.total_frames + original_episodes = sample_dataset.meta.total_episodes + original_fps = sample_dataset.meta.fps + + modified_dataset = modify_tasks(sample_dataset, new_task="New task") + + # Verify other metadata is preserved + assert modified_dataset.meta.total_frames == original_frames + assert modified_dataset.meta.total_episodes == original_episodes + assert modified_dataset.meta.fps == original_fps + + +def test_modify_tasks_task_index_correct(sample_dataset): + """Test that task_index values are correct in data files.""" + # Create tasks that will have predictable indices (sorted alphabetically) + episode_tasks = { + 0: "Alpha task", # Will be index 0 + 1: "Beta task", # Will be index 1 + 2: "Alpha task", # Will be index 0 + 3: "Gamma task", # Will be index 2 + 4: "Beta task", # Will be index 1 + } + + modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks) + + # Verify task indices are correct + task_to_expected_idx = { + "Alpha task": 0, + "Beta task": 1, + "Gamma task": 2, + } + + for i in range(len(modified_dataset)): + item = modified_dataset[i] + ep_idx = item["episode_index"].item() + expected_task = episode_tasks[ep_idx] + expected_idx = task_to_expected_idx[expected_task] + assert item["task_index"].item() == expected_idx + assert item["task"] == expected_task + + +def test_modify_tasks_in_place(sample_dataset): + """Test that modify_tasks modifies the dataset in-place.""" + original_root = sample_dataset.root + + modified_dataset = modify_tasks(sample_dataset, new_task="New task") + + # Verify same instance is returned and root is unchanged + assert modified_dataset is sample_dataset + assert modified_dataset.root == original_root + + +def test_modify_tasks_keeps_original_when_not_overridden(sample_dataset): + """Test that original tasks are kept when using episode_tasks without new_task.""" + from lerobot.datasets.utils import load_episodes + + # Ensure episodes metadata is loaded + if sample_dataset.meta.episodes is None: + sample_dataset.meta.episodes = load_episodes(sample_dataset.meta.root) + + # Get original tasks for episodes not being overridden + original_task_ep0 = sample_dataset.meta.episodes[0]["tasks"][0] + original_task_ep1 = sample_dataset.meta.episodes[1]["tasks"][0] + + # Only override episodes 2, 3, 4 + episode_tasks = {2: "New Task A", 3: "New Task B", 4: "New Task A"} + + modified_dataset = modify_tasks(sample_dataset, episode_tasks=episode_tasks) + + # Verify original tasks are kept for episodes 0 and 1 + assert modified_dataset.meta.episodes[0]["tasks"][0] == original_task_ep0 + assert modified_dataset.meta.episodes[1]["tasks"][0] == original_task_ep1 + + # Verify new tasks for overridden episodes + assert modified_dataset.meta.episodes[2]["tasks"][0] == "New Task A" + assert modified_dataset.meta.episodes[3]["tasks"][0] == "New Task B" + assert modified_dataset.meta.episodes[4]["tasks"][0] == "New Task A" + + def test_convert_image_to_video_dataset(tmp_path): """Test converting lerobot/pusht_image dataset to video format.""" from lerobot.datasets.lerobot_dataset import LeRobotDataset diff --git a/tests/datasets/test_image_transforms.py b/tests/datasets/test_image_transforms.py index 8a66ceb24..ef7e8c395 100644 --- a/tests/datasets/test_image_transforms.py +++ b/tests/datasets/test_image_transforms.py @@ -390,6 +390,30 @@ def test_sharpness_jitter_invalid_range_max_smaller(): SharpnessJitter((2.0, 0.1)) +def test_make_transform_from_config_with_v2_resize(img_tensor_factory): + img_tensor = img_tensor_factory() + tf_cfg = ImageTransformConfig(type="Resize", kwargs={"size": (32, 32)}) + tf = make_transform_from_config(tf_cfg) + assert isinstance(tf, v2.Resize) + output = tf(img_tensor) + assert output.shape[-2:] == (32, 32) + + +def test_make_transform_from_config_with_v2_identity(img_tensor_factory): + img_tensor = img_tensor_factory() + tf_cfg = ImageTransformConfig(type="Identity", kwargs={}) + tf = make_transform_from_config(tf_cfg) + assert isinstance(tf, v2.Identity) + output = tf(img_tensor) + assert output.shape == img_tensor.shape + + +def test_make_transform_from_config_invalid_type(): + tf_cfg = ImageTransformConfig(type="NotARealTransform", kwargs={}) + with pytest.raises(ValueError, match="not valid"): + make_transform_from_config(tf_cfg) + + def test_save_all_transforms(img_tensor_factory, tmp_path): img_tensor = img_tensor_factory() tf_cfg = ImageTransformsConfig(enable=True) diff --git a/tests/datasets/test_subtask_dataset.py b/tests/datasets/test_subtask_dataset.py new file mode 100644 index 000000000..f80a6c72d --- /dev/null +++ b/tests/datasets/test_subtask_dataset.py @@ -0,0 +1,190 @@ +#!/usr/bin/env python + +# Copyright 2026 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Tests for subtask functionality in LeRobotDataset. + +These tests verify that: +- Subtask information is correctly loaded from datasets that have subtask data +- The __getitem__ method correctly adds subtask strings to returned items +- Subtask handling gracefully handles missing data +""" + +import pandas as pd +import pytest +import torch + +from lerobot.datasets.lerobot_dataset import LeRobotDataset + + +class TestSubtaskDataset: + """Tests for subtask handling in LeRobotDataset.""" + + @pytest.fixture + def subtask_dataset(self): + """Load the test subtask dataset from the hub.""" + # Use lerobot/pusht-subtask dataset with episode 1 + return LeRobotDataset( + repo_id="lerobot/pusht-subtask", + episodes=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + ) + + def test_subtask_dataset_loads(self, subtask_dataset): + """Test that the subtask dataset loads successfully.""" + assert subtask_dataset is not None + assert len(subtask_dataset) > 0 + + def test_subtask_metadata_loaded(self, subtask_dataset): + """Test that subtask metadata is loaded when present in dataset.""" + # The dataset should have subtasks metadata loaded + assert subtask_dataset.meta.subtasks is not None + assert isinstance(subtask_dataset.meta.subtasks, pd.DataFrame) + + def test_subtask_index_in_features(self, subtask_dataset): + """Test that subtask_index is a feature when dataset has subtasks.""" + assert "subtask_index" in subtask_dataset.features + + def test_getitem_returns_subtask_string(self, subtask_dataset): + """Test that __getitem__ correctly adds subtask string to returned item.""" + item = subtask_dataset[0] + + # Subtask should be present in the returned item + assert "subtask" in item + assert isinstance(item["subtask"], str) + assert len(item["subtask"]) > 0 # Should not be empty + + def test_getitem_has_subtask_index(self, subtask_dataset): + """Test that __getitem__ includes subtask_index.""" + item = subtask_dataset[0] + + assert "subtask_index" in item + assert isinstance(item["subtask_index"], torch.Tensor) + + def test_subtask_index_maps_to_valid_subtask(self, subtask_dataset): + """Test that subtask_index correctly maps to a subtask in metadata.""" + item = subtask_dataset[0] + + subtask_idx = item["subtask_index"].item() + subtask_from_metadata = subtask_dataset.meta.subtasks.iloc[subtask_idx].name + + assert item["subtask"] == subtask_from_metadata + + def test_all_items_have_subtask(self, subtask_dataset): + """Test that all items in the dataset have subtask information.""" + for i in range(min(len(subtask_dataset), 5)): # Check first 5 items + item = subtask_dataset[i] + assert "subtask" in item + assert isinstance(item["subtask"], str) + + def test_task_and_subtask_coexist(self, subtask_dataset): + """Test that both task and subtask are present in returned items.""" + item = subtask_dataset[0] + + # Both task and subtask should be present + assert "task" in item + assert "subtask" in item + assert isinstance(item["task"], str) + assert isinstance(item["subtask"], str) + + +class TestSubtaskDatasetMissing: + """Tests for graceful handling when subtask data is missing.""" + + @pytest.fixture + def dataset_without_subtasks(self, tmp_path, empty_lerobot_dataset_factory): + """Create a dataset without subtask information.""" + features = {"state": {"dtype": "float32", "shape": (2,), "names": None}} + dataset = empty_lerobot_dataset_factory(root=tmp_path / "no_subtask", features=features) + + # Add some frames and save + for _ in range(5): + dataset.add_frame({"state": torch.randn(2), "task": "Test task"}) + dataset.save_episode() + dataset.finalize() + + # Reload the dataset + return LeRobotDataset(dataset.repo_id, root=dataset.root) + + def test_no_subtask_in_features(self, dataset_without_subtasks): + """Test that subtask_index is not in features when not provided.""" + assert "subtask_index" not in dataset_without_subtasks.features + + def test_getitem_without_subtask(self, dataset_without_subtasks): + """Test that __getitem__ works when subtask is not present.""" + item = dataset_without_subtasks[0] + + # Item should still be retrievable + assert item is not None + assert "state" in item + assert "task" in item + + # Subtask should NOT be present + assert "subtask" not in item + + def test_subtasks_metadata_is_none(self, dataset_without_subtasks): + """Test that subtasks metadata is None when not present.""" + assert dataset_without_subtasks.meta.subtasks is None + + +class TestSubtaskEdgeCases: + """Edge case tests for subtask handling.""" + + def test_subtask_with_multiple_episodes(self): + """Test subtask handling with multiple episodes if available.""" + try: + dataset = LeRobotDataset( + repo_id="lerobot/pusht-subtask", + episodes=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + ) + except Exception: + pytest.skip("Could not load test-subtask dataset") + + # Check first and last items have valid subtasks + first_item = dataset[0] + last_item = dataset[len(dataset) - 1] + + assert "subtask" in first_item + assert "subtask" in last_item + assert isinstance(first_item["subtask"], str) + assert isinstance(last_item["subtask"], str) + + def test_subtask_index_consistency(self): + """Test that same subtask_index returns same subtask string.""" + try: + dataset = LeRobotDataset( + repo_id="lerobot/pusht-subtask", + episodes=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], + ) + except Exception: + pytest.skip("Could not load test-subtask dataset") + + if len(dataset) < 2: + pytest.skip("Dataset too small for this test") + + # Collect subtask_index to subtask mappings + subtask_map = {} + for i in range(min(len(dataset), 10)): + item = dataset[i] + idx = item["subtask_index"].item() + subtask = item["subtask"] + + if idx in subtask_map: + # Same index should always return same subtask + assert subtask_map[idx] == subtask, ( + f"Inconsistent subtask for index {idx}: '{subtask_map[idx]}' vs '{subtask}'" + ) + else: + subtask_map[idx] = subtask diff --git a/tests/policies/test_sac_policy.py b/tests/policies/test_sac_policy.py index 8576883bd..6fad2979e 100644 --- a/tests/policies/test_sac_policy.py +++ b/tests/policies/test_sac_policy.py @@ -441,12 +441,13 @@ def test_sac_policy_with_predefined_entropy(): def test_sac_policy_update_temperature(): + """Test that temperature property is always in sync with log_alpha.""" config = create_default_config(continuous_action_dim=10, state_dim=10) policy = SACPolicy(config=config) assert policy.temperature == pytest.approx(1.0) policy.log_alpha.data = torch.tensor([math.log(0.1)]) - policy.update_temperature() + # Temperature property automatically reflects log_alpha changes assert policy.temperature == pytest.approx(0.1) diff --git a/tests/processor/test_tokenizer_processor.py b/tests/processor/test_tokenizer_processor.py index d6f87f567..64cc8aac8 100644 --- a/tests/processor/test_tokenizer_processor.py +++ b/tests/processor/test_tokenizer_processor.py @@ -27,7 +27,14 @@ import torch from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature from lerobot.processor import DataProcessorPipeline, TokenizerProcessorStep, TransitionKey from lerobot.processor.converters import create_transition, identity_transition -from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_LANGUAGE, OBS_STATE +from lerobot.utils.constants import ( + ACTION, + OBS_IMAGE, + OBS_LANGUAGE, + OBS_LANGUAGE_SUBTASK_ATTENTION_MASK, + OBS_LANGUAGE_SUBTASK_TOKENS, + OBS_STATE, +) from tests.utils import require_package @@ -1038,3 +1045,459 @@ def test_simulated_accelerate_scenario(): # MockTokenizer squeezes single-item batches, so shape is (max_length,) not (1, max_length) assert tokens.shape == (10,) # MockTokenizer behavior for single string in list assert attention_mask.shape == (10,) + + +# ============================================================================= +# Tests for get_subtask method +# ============================================================================= + + +@require_package("transformers") +def test_get_subtask_missing_key(): + """Test get_subtask returns None when subtask key is missing from complementary_data.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task"}, # No "subtask" key + ) + + result = processor.get_subtask(transition) + assert result is None + + +@require_package("transformers") +def test_get_subtask_none_value(): + """Test get_subtask returns None when subtask value is None.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": None}, + ) + + result = processor.get_subtask(transition) + assert result is None + + +@require_package("transformers") +def test_get_subtask_none_complementary_data(): + """Test get_subtask returns None when complementary_data is None.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data=None, # No complementary data + ) + + result = processor.get_subtask(transition) + assert result is None + + +@require_package("transformers") +def test_get_subtask_string(): + """Test get_subtask returns list with single string when subtask is a string.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": "pick up the cube"}, + ) + + result = processor.get_subtask(transition) + assert result == ["pick up the cube"] + assert isinstance(result, list) + assert len(result) == 1 + + +@require_package("transformers") +def test_get_subtask_list_of_strings(): + """Test get_subtask returns the list when subtask is already a list of strings.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + subtask_list = ["pick up", "move to target", "place down"] + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": subtask_list}, + ) + + result = processor.get_subtask(transition) + assert result == subtask_list + assert isinstance(result, list) + assert len(result) == 3 + + +@require_package("transformers") +def test_get_subtask_unsupported_type_integer(): + """Test get_subtask returns None when subtask is an unsupported type (integer).""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": 123}, + ) + + result = processor.get_subtask(transition) + assert result is None + + +@require_package("transformers") +def test_get_subtask_unsupported_type_mixed_list(): + """Test get_subtask returns None when subtask is a list with mixed types.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": ["valid string", 123, "another string"]}, + ) + + result = processor.get_subtask(transition) + assert result is None + + +@require_package("transformers") +def test_get_subtask_unsupported_type_dict(): + """Test get_subtask returns None when subtask is a dictionary.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": {"key": "value"}}, + ) + + result = processor.get_subtask(transition) + assert result is None + + +@require_package("transformers") +def test_get_subtask_empty_string(): + """Test get_subtask with empty string returns list with empty string.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": ""}, + ) + + result = processor.get_subtask(transition) + assert result == [""] + + +@require_package("transformers") +def test_get_subtask_empty_list(): + """Test get_subtask with empty list returns empty list.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": []}, + ) + + result = processor.get_subtask(transition) + assert result == [] + + +# ============================================================================= +# Tests for subtask tokenization in observation method +# ============================================================================= + + +@require_package("transformers") +def test_subtask_tokenization_when_present(): + """Test that subtask is tokenized and added to observation when present.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=8) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": "pick up the red cube"}, + ) + + result = processor(transition) + + # Check that subtask tokens were added to observation + observation = result[TransitionKey.OBSERVATION] + assert OBS_LANGUAGE_SUBTASK_TOKENS in observation + assert OBS_LANGUAGE_SUBTASK_ATTENTION_MASK in observation + + # Check token structure + subtask_tokens = observation[OBS_LANGUAGE_SUBTASK_TOKENS] + subtask_attention_mask = observation[OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] + assert isinstance(subtask_tokens, torch.Tensor) + assert isinstance(subtask_attention_mask, torch.Tensor) + assert subtask_tokens.shape == (8,) + assert subtask_attention_mask.shape == (8,) + assert subtask_attention_mask.dtype == torch.bool + + +@require_package("transformers") +def test_subtask_tokenization_not_added_when_none(): + """Test that subtask tokens are NOT added to observation when subtask is None.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=8) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task"}, # No subtask + ) + + result = processor(transition) + + # Check that subtask tokens were NOT added to observation + observation = result[TransitionKey.OBSERVATION] + assert OBS_LANGUAGE_SUBTASK_TOKENS not in observation + assert OBS_LANGUAGE_SUBTASK_ATTENTION_MASK not in observation + + # But main task tokens should still be present + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + + +@require_package("transformers") +def test_subtask_tokenization_not_added_when_subtask_value_is_none(): + """Test that subtask tokens are NOT added when subtask value is explicitly None.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=8) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": None}, + ) + + result = processor(transition) + + # Check that subtask tokens were NOT added to observation + observation = result[TransitionKey.OBSERVATION] + assert OBS_LANGUAGE_SUBTASK_TOKENS not in observation + assert OBS_LANGUAGE_SUBTASK_ATTENTION_MASK not in observation + + +@require_package("transformers") +def test_subtask_tokenization_list_of_strings(): + """Test subtask tokenization with list of strings.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=8) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": ["pick up", "place down"]}, + ) + + result = processor(transition) + + # Check that subtask tokens were added to observation + observation = result[TransitionKey.OBSERVATION] + assert OBS_LANGUAGE_SUBTASK_TOKENS in observation + assert OBS_LANGUAGE_SUBTASK_ATTENTION_MASK in observation + + # Check token structure for batch + subtask_tokens = observation[OBS_LANGUAGE_SUBTASK_TOKENS] + subtask_attention_mask = observation[OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] + assert subtask_tokens.shape == (2, 8) # batch_size=2, seq_len=8 + assert subtask_attention_mask.shape == (2, 8) + + +@require_package("transformers") +def test_subtask_tokenization_device_cpu(): + """Test that subtask tokens are on CPU when other tensors are on CPU.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Create transition with CPU tensors + observation = {OBS_STATE: torch.randn(10)} # CPU tensor + action = torch.randn(5) # CPU tensor + transition = create_transition( + observation=observation, + action=action, + complementary_data={"task": "main task", "subtask": "pick up cube"}, + ) + + result = processor(transition) + + # Check that subtask tokens are on CPU + subtask_tokens = result[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_TOKENS] + subtask_attention_mask = result[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] + + assert subtask_tokens.device.type == "cpu" + assert subtask_attention_mask.device.type == "cpu" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@require_package("transformers") +def test_subtask_tokenization_device_cuda(): + """Test that subtask tokens are moved to CUDA when other tensors are on CUDA.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + # Create transition with CUDA tensors + observation = {OBS_STATE: torch.randn(10).cuda()} # CUDA tensor + action = torch.randn(5).cuda() # CUDA tensor + transition = create_transition( + observation=observation, + action=action, + complementary_data={"task": "main task", "subtask": "pick up cube"}, + ) + + result = processor(transition) + + # Check that subtask tokens are on CUDA + subtask_tokens = result[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_TOKENS] + subtask_attention_mask = result[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] + + assert subtask_tokens.device.type == "cuda" + assert subtask_attention_mask.device.type == "cuda" + + +@require_package("transformers") +def test_subtask_tokenization_preserves_other_observation_data(): + """Test that subtask tokenization preserves other observation data.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + original_state = torch.tensor([1.0, 2.0, 3.0]) + transition = create_transition( + observation={"state": original_state.clone()}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": "pick up cube"}, + ) + + result = processor(transition) + observation = result[TransitionKey.OBSERVATION] + + # Check that original observation data is preserved + assert torch.equal(observation["state"], original_state) + + # Check that both task and subtask tokens are present + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + assert OBS_LANGUAGE_SUBTASK_TOKENS in observation + assert OBS_LANGUAGE_SUBTASK_ATTENTION_MASK in observation + + +@require_package("transformers") +def test_subtask_attention_mask_dtype(): + """Test that subtask attention mask has correct dtype (bool).""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": "pick up cube"}, + ) + + result = processor(transition) + observation = result[TransitionKey.OBSERVATION] + + subtask_attention_mask = observation[OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] + assert subtask_attention_mask.dtype == torch.bool + + +@require_package("transformers") +def test_subtask_tokenization_deterministic(): + """Test that subtask tokenization is deterministic for the same input.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=10) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": "consistent subtask"}, + ) + + result1 = processor(transition) + result2 = processor(transition) + + subtask_tokens1 = result1[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_TOKENS] + subtask_tokens2 = result2[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_TOKENS] + subtask_mask1 = result1[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] + subtask_mask2 = result2[TransitionKey.OBSERVATION][OBS_LANGUAGE_SUBTASK_ATTENTION_MASK] + + # Results should be identical + assert torch.equal(subtask_tokens1, subtask_tokens2) + assert torch.equal(subtask_mask1, subtask_mask2) + + +@require_package("transformers") +@patch("lerobot.processor.tokenizer_processor.AutoTokenizer") +def test_subtask_tokenization_integration_with_pipeline(mock_auto_tokenizer): + """Test subtask tokenization works correctly with DataProcessorPipeline.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer + + tokenizer_processor = TokenizerProcessorStep(tokenizer_name="test-tokenizer", max_length=6) + robot_processor = DataProcessorPipeline( + [tokenizer_processor], to_transition=identity_transition, to_output=identity_transition + ) + + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": "subtask instruction"}, + ) + + result = robot_processor(transition) + + # Check that observation exists and both tokenizations were applied + assert TransitionKey.OBSERVATION in result + observation = result[TransitionKey.OBSERVATION] + + # Check task tokens + assert f"{OBS_LANGUAGE}.tokens" in observation + assert f"{OBS_LANGUAGE}.attention_mask" in observation + + # Check subtask tokens + assert OBS_LANGUAGE_SUBTASK_TOKENS in observation + assert OBS_LANGUAGE_SUBTASK_ATTENTION_MASK in observation + + # Check shapes + assert observation[f"{OBS_LANGUAGE}.tokens"].shape == (6,) + assert observation[OBS_LANGUAGE_SUBTASK_TOKENS].shape == (6,) + + +@require_package("transformers") +def test_subtask_not_added_for_unsupported_types(): + """Test that subtask tokens are not added when subtask has unsupported type.""" + mock_tokenizer = MockTokenizer(vocab_size=100) + processor = TokenizerProcessorStep(tokenizer=mock_tokenizer, max_length=8) + + # Test with integer subtask + transition = create_transition( + observation={"state": torch.tensor([1.0, 2.0])}, + action=torch.tensor([0.1, 0.2]), + complementary_data={"task": "main task", "subtask": 123}, + ) + + result = processor(transition) + observation = result[TransitionKey.OBSERVATION] + + # Subtask tokens should NOT be added for unsupported types + assert OBS_LANGUAGE_SUBTASK_TOKENS not in observation + assert OBS_LANGUAGE_SUBTASK_ATTENTION_MASK not in observation + + # But main task tokens should still be present + assert f"{OBS_LANGUAGE}.tokens" in observation