add quick AI draft for quickstart

add new docs chapters structure
2026-05-30 16:09:44 +00:00 · 2026-05-26 13:10:24 +02:00 · 2026-05-26 12:01:33 +02:00
9 changed files with 571 additions and 293 deletions
@@ -0,0 +1,172 @@
 - sections:
  - local: index
    title: LeRobot
  - local: installation
    title: Installation
  - local: cheat-sheet
    title: Cheat sheet
  title: Get started
 - sections:
  - local: il_robots
    title: Imitation Learning for Robots
  - local: bring_your_own_policies
    title: Adding a Policy
  - local: integrate_hardware
    title: Bring Your Own Hardware
  - local: hilserl
    title: Train a Robot with RL
  - local: hilserl_sim
    title: Train RL in Simulation
  - local: multi_gpu_training
    title: Multi GPU training
  - local: hil_data_collection
    title: Human In the Loop Data Collection
  - local: peft_training
    title: Training with PEFT (e.g., LoRA)
  - local: rename_map
    title: Using Rename Map and Empty Cameras
  title: "Tutorials"
 - sections:
  - local: hardware_guide
    title: Compute Hardware Guide
  - local: torch_accelerators
    title: PyTorch accelerators
  title: "Compute & Hardware"
 - sections:
  - local: lerobot-dataset-v3
    title: Using LeRobotDataset
  - local: porting_datasets_v3
    title: Porting Large Datasets
  - local: using_dataset_tools
    title: Using the Dataset Tools
  - local: language_and_recipes
    title: Language Columns and Recipes
  - local: tools
    title: Tools
  - local: video_encoding_parameters
    title: Video encoding parameters
  - local: streaming_video_encoding
    title: Streaming Video Encoding
  title: "Datasets"
 - sections:
  - local: act
    title: ACT
  - local: smolvla
    title: SmolVLA
  - local: pi0
    title: π₀ (Pi0)
  - local: pi0fast
    title: π₀-FAST (Pi0Fast)
  - local: pi05
    title: π₀.₅ (Pi05)
  - local: eo1
    title: EO-1
  - local: groot
    title: NVIDIA GR00T N1.5
  - local: xvla
    title: X-VLA
  - local: multi_task_dit
    title: Multitask DiT Policy
  - local: walloss
    title: WALL-OSS
  title: "Policies"
 - sections:
  - local: sarm
    title: SARM
  title: "Reward Models"
 - sections:
  - local: inference
    title: Policy Deployment (lerobot-rollout)
  - local: async
    title: Use Async Inference
  - local: rtc
    title: Real-Time Chunking (RTC)
  title: "Inference"
 - sections:
  - local: envhub
    title: Environments from the Hub
  - local: envhub_leisaac
    title: Control & Train Robots in Sim (LeIsaac)
  title: "Simulation"
 - sections:
  - local: adding_benchmarks
    title: Adding a New Benchmark
  - local: libero
    title: LIBERO
  - local: libero_plus
    title: LIBERO-plus
  - local: metaworld
    title: Meta-World
  - local: robotwin
    title: RoboTwin 2.0
  - local: robocasa
    title: RoboCasa365
  - local: robocerebra
    title: RoboCerebra
  - local: robomme
    title: RoboMME
  - local: envhub_isaaclab_arena
    title: NVIDIA IsaacLab Arena Environments
  - local: vlabench
    title: VLABench
  title: "Benchmarks"
 - sections:
  - local: introduction_processors
    title: Introduction to Robot Processors
  - local: debug_processor_pipeline
    title: Debug your processor pipeline
  - local: implement_your_own_processor
    title: Implement your own processor
  - local: processors_robots_teleop
    title: Processors for Robots and Teleoperators
  - local: env_processor
    title: Environment Processors
  - local: action_representations
    title: Action Representations
  title: "Robot Processors"
 - sections:
  - local: so101
    title: SO-101
  - local: so100
    title: SO-100
  - local: koch
    title: Koch v1.1
  - local: lekiwi
    title: LeKiwi
  - local: hope_jr
    title: Hope Jr
  - local: reachy2
    title: Reachy 2
  - local: unitree_g1
    title: Unitree G1
  - local: earthrover_mini_plus
    title: Earth Rover Mini
  - local: omx
    title: OMX
  - local: openarm
    title: OpenArm
  - local: rebot_b601
    title: reBot B601-DM
  title: "Robots"
 - sections:
  - local: phone_teleop
    title: Phone
  title: "Teleoperators"
 - sections:
  - local: cameras
    title: Cameras
  title: "Sensors"
 - sections:
  - local: notebooks
    title: Notebooks
  - local: feetech
    title: Updating Feetech Firmware
  - local: damiao
    title: Damiao Motors and CAN Bus
  title: "Resources"
 - sections:
  - local: contributing
    title: Contribute to LeRobot
  - local: backwardcomp
    title: Backward compatibility
  title: "About"
@@ -1,56 +1,68 @@
 # LeRobot documentation table of contents
 #
 # Ordering principle: gentle onboarding first, advanced/custom work last.
 # Within each top-level section the same rule applies — concept/overview pages
 # before reference/per-item pages.
 #
 # Pages marked "NEW (to create)" do not yet exist as .mdx files; they are
 # placeholders for the redesign and must be authored before the docs build.
 - sections:
  - local: index
-    title: LeRobot
+    title: 🤗 LeRobot
  - local: quickstart           # NEW (to create) — 15-min zero-to-trained-ACT path
    title: Quickstart
  - local: installation
    title: Installation
  - local: core_concepts        # NEW (to create) — datasets, policies, processors, robots, envs in one mental model
    title: Core concepts
  - local: cheat-sheet
-    title: Cheat sheet
+    title: Command cheat sheet
  title: Get started
 - sections:
  - local: il_robots
-    title: Imitation Learning for Robots
+    title: Imitation learning end-to-end
  - local: lelab
    title: LeLab - Lerobot GUI
  - local: bring_your_own_policies
    title: Adding a Policy
  - local: integrate_hardware
    title: Bring Your Own Hardware
  - local: hilserl
    title: Train a Robot with RL
  - local: hilserl_sim
    title: Train RL in Simulation
  - local: multi_gpu_training
    title: Multi GPU training
  - local: hil_data_collection
-    title: Human In the Loop Data Collection
+    title: Human-in-the-loop data collection
-  - local: peft_training
+  - local: inference
-    title: Training with PEFT (e.g., LoRA)
+    title: Deploying a trained policy
  - local: rename_map
-    title: Using Rename Map and Empty Cameras
+    title: Matching dataset keys to a policy (rename map)
-  title: "Tutorials"
+  title: Your first project
 - sections:
  - local: hardware_guide
-    title: Compute Hardware Guide
+    title: Compute hardware guide
  - local: torch_accelerators
    title: PyTorch accelerators
-  title: "Compute & Hardware"
+  - local: multi_gpu_training
    title: Multi-GPU training
  - local: peft_training
    title: Parameter-efficient fine-tuning (LoRA)
  title: Training
 - sections:
  - local: lerobot-dataset-v3
    title: Using LeRobotDataset
  - local: porting_datasets_v3
    title: Porting Large Datasets
  - local: using_dataset_tools
-    title: Using the Dataset Tools
+    title: Dataset tools
  - local: language_and_recipes
-    title: Language Columns and Recipes
+    title: Language columns & recipes
  - local: tools
-    title: Tools
+    title: Tool calls in datasets
  - local: video_encoding_parameters
    title: Video encoding parameters
  - local: streaming_video_encoding
-    title: Streaming Video Encoding
+    title: Streaming video encoding
-  title: "Datasets"
+  - local: porting_datasets_v3
    title: Porting datasets to v3
  title: Datasets
 - sections:
  - local: policies_overview    # NEW (to create) — concept hub + "choose a policy" decision guide
    title: Choosing a policy
  - sections:
    - local: act
      title: ACT
    - local: smolvla
@@ -58,7 +70,7 @@
    - local: pi0
      title: π₀ (Pi0)
    - local: pi0fast
-    title: π₀-FAST (Pi0Fast)
+      title: π₀-FAST
    - local: pi05
      title: π₀.₅ (Pi05)
    - local: eo1
@@ -67,32 +79,37 @@
      title: NVIDIA GR00T N1.5
    - local: xvla
      title: X-VLA
  - local: multi_task_dit
    title: Multitask DiT Policy
    - local: walloss
      title: WALL-OSS
-  title: "Policies"
+    - local: multi_task_dit
      title: Multitask DiT
    title: Policy reference
  title: Policies
 - sections:
  - local: sarm
    title: SARM
  title: "Reward Models"
 - sections:
  - local: inference
    title: Policy Deployment (lerobot-rollout)
  - local: async
-    title: Use Async Inference
+    title: Async inference
  - local: rtc
-    title: Real-Time Chunking (RTC)
+    title: Real-time chunking (RTC)
-  title: "Inference"
+  title: Real-time deployment
 - sections:
  - local: hilserl
    title: Train a robot with RL (HIL-SERL)
  - local: hilserl_sim
    title: Train RL in simulation
  - local: sarm
    title: SARM reward model
  title: Reinforcement learning
 - sections:
  - local: envhub
    title: Environments from the Hub
  - local: envhub_leisaac
-    title: Control & Train Robots in Sim (LeIsaac)
+    title: LeIsaac — control & train in sim
-  title: "Simulation"
+  - local: envhub_isaaclab_arena
- sections:
+    title: NVIDIA IsaacLab Arena environments
-  - local: adding_benchmarks
+  - sections:
    title: Adding a New Benchmark
    - local: libero
      title: LIBERO
    - local: libero_plus
@@ -107,68 +124,91 @@
      title: RoboCerebra
    - local: robomme
      title: RoboMME
  - local: envhub_isaaclab_arena
    title: NVIDIA IsaacLab Arena Environments
    - local: vlabench
      title: VLABench
-  title: "Benchmarks"
+    title: Benchmark suites
  title: Simulation & benchmarks
 - sections:
  - local: introduction_processors
-    title: Introduction to Robot Processors
+    title: Introduction to processors
  - local: debug_processor_pipeline
    title: Debug your processor pipeline
  - local: implement_your_own_processor
    title: Implement your own processor
  - local: processors_robots_teleop
-    title: Processors for Robots and Teleoperators
+    title: Processors for robots & teleoperators
  - local: env_processor
-    title: Environment Processors
+    title: Environment processors
  - local: action_representations
-    title: Action Representations
+    title: Action representations
-  title: "Robot Processors"
+  - local: debug_processor_pipeline
    title: Debugging a pipeline
  - local: implement_your_own_processor
    title: Implementing your own processor
  title: Processors
 - sections:
  - sections:
    - local: so101
      title: SO-101
    - local: so100
      title: SO-100
    - local: koch
      title: Koch v1.1
    - local: omx
      title: OMX
    - local: openarm
      title: OpenArm
    title: Low-cost arms
  - sections:
    - local: lekiwi
      title: LeKiwi
    - local: earthrover_mini_plus
      title: Earth Rover Mini
    title: Mobile platforms
  - sections:
    - local: hope_jr
      title: Hope Jr
    - local: reachy2
      title: Reachy 2
    - local: unitree_g1
      title: Unitree G1
-  - local: earthrover_mini_plus
+    title: Bimanual & humanoid
-    title: Earth Rover Mini
+  - sections:
  - local: omx
    title: OMX
  - local: openarm
    title: OpenArm
    - local: rebot_b601
      title: reBot B601-DM
-  title: "Robots"
+    title: Research & industrial
- sections:
+  title: Supported robots
-  - local: phone_teleop
+
    title: Phone
  title: "Teleoperators"
 - sections:
  - local: cameras
    title: Cameras
-  title: "Sensors"
+  - local: phone_teleop
- sections:
+    title: Phone teleoperation
  - local: notebooks
    title: Notebooks
  - local: feetech
-    title: Updating Feetech Firmware
+    title: Feetech firmware update
  - local: damiao
-    title: Damiao Motors and CAN Bus
+    title: Damiao motors & CAN bus
-  title: "Resources"
+  title: Sensors, teleop & motors
 - sections:
-  - local: contributing
+  - local: integrate_hardware
-    title: Contribute to LeRobot
+    title: Bring your own hardware
  - local: bring_your_own_policies
    title: Add a new policy
  - local: adding_benchmarks
    title: Add a new benchmark
  title: Extend LeRobot
 - sections:
  - local: troubleshooting       # NEW (to create) — common errors: USB, calibration drift, CUDA OOM, video decoding…
    title: Troubleshooting & FAQ
  - local: glossary              # NEW (to create) — episode, action chunk, leader/follower, teleop, processor…
    title: Glossary
  - local: notebooks
    title: Example notebooks
  - local: backwardcomp
    title: Backward compatibility
-  title: "About"
+  title: Reference
 - sections:
  - local: contributing
    title: Contributing to LeRobot
  title: About
@@ -1,42 +0,0 @@
 # LeLab - LeRobot Guide
 Graphical user interfaces are the easiest to use for beginners because it's easy to just click everything without remembering the proper commands. That's why we built LeLab which is a GUI built on top of the LeRobot library. With this app you will be able to add robots, collect datasets, train and deploy models.
 ### Installation
 To install lerobot you can simply copy the following command and paste into your terminal. For it to work you need to have `uv` installed, [here is how to do it.](https://docs.astral.sh/uv/getting-started/installation/) 
 ```
 uv tool install git+https://github.com/huggingface/leLab.git && lelab
 ```
 Once installed you will be able to run lelab anytime you want with `lelab` command from your terminal (above command has it included at the end so it will run it right after installation).
 ### Adding robots
 ##### Calibration
 You will need to select the proper arm type (leader or follower) and calibrate each arm as shown in the video available inside LeLab. Make sure that all joints are in the middle position when starting the calibration.
 ##### Adding cameras
 At the bottom of the add robot page you can also add the cameras and name them accordingly. 
 ### Teleoperation
 Once the robots have been configured you can go back and click the teleoperation button. You will see the 3D visualization of the arm and will be able to control the follower with the leader. If something doesn't work there, remove and add your robot again following the steps described in LeLab.
 ### Recording a dataset
 Type a new name for your dataset and press on the plus button. You will need to provide:
 - Task description, for example "put the cube in a container"
 - Number of episodes that you want to record, at least 30 recommended
 - Episode and reset durations. These are max durations and can be shortened while recording with a spacebar press.
 - If you configured your cameras earlier you don't need to do that again.
 Press start recording, wait for it to load, perform the task with confident movements but don't rush. Once the task is finished and you moved your robot to the initial position press the spacebar. You will have time to reset the environment for example grab the cube from the container and placing it on the desk again. Once ready press the spacebar and record the next episode. Repeat until all the episodes are recorded.
 ### Training a model
 This is the most powerful function with LeLab! You can easily train models locally on your own computer but also with [HF Jobs](https://huggingface.co/docs/huggingface_hub/en/guides/jobs) which gives you easy access to very powerful GPUs with clear pricing.
 > [!TIP]
 > To use HF Jobs make sure that you are logged in to HF, you can do that by running `hf auth login` in the terminal.
 In the training tab select if you want to train locally or specific HF hardware you want to use. You will also need to provide the dataset that will be used for training. Your own datasets will be listed in a dropdown list, you can also use other datasets by providing its id. Set the policy you want to train, batch size and number of steps. For guide on choosing hardware and batch size check out our [Compute HW Guide for LeRobot Training.](hardware_guide.mdx) 
 Once you start training the progress will be visualized inside LeLab. Checkpoints will be saved as well.
 ### Running the model on a robot
 In the main view of the LeLab under jobs you will see all the models that you trained. To run the policy on the robot just click the green run button and press start inference. After loading the policy the robot should start solving the task that it learned during training.
@@ -0,0 +1,219 @@
 # Quickstart
 This is the **shortest path** from an unboxed SO-101 to a policy that drives your own robot. Every step is copy-paste; replace the **`<placeholders>`** with the values for your setup.
 By the end you will have:
 - A calibrated SO-101 leader + follower pair.
 - A dataset of 30 episodes pushed to the Hugging Face Hub.
 - A trained ACT policy (~20k steps) running on your robot via `lerobot-rollout`.
 > [!NOTE]
 > **How long will this take?**
 > Recording 30 episodes is roughly 30–60 minutes of teleoperation. Training ACT for 20k steps takes ~1.5h on an A100, a few hours on a laptop RTX 3060, longer on Apple Silicon (`mps`). The commands themselves are quick — most of the wall-clock is data collection and training.
 > [!TIP]
 > If you only want to **understand the codebase** or **train on an existing dataset without hardware**, this page isn't for you. Read [Core concepts](./core_concepts) first, then jump to [Imitation learning end-to-end](./il_robots).
 ---
 ## Before you start
 You need:
 - An **assembled SO-101 leader + follower pair**. If your robot is not assembled yet, follow the [SO-101 assembly guide](./so101) and come back here.
 - **One or two cameras** (USB webcam works fine).
 - A **CUDA GPU with ≥ 6 GB VRAM** (ACT is light — a laptop RTX 3060 works). Apple Silicon (`mps`) and CPU are supported but slower. See the [compute hardware guide](./hardware_guide) for sizing.
 - A **Hugging Face account** — datasets and the trained policy will be pushed to your Hub.
 If any of the above is missing, fix it first; the rest of the page assumes it.
 ---
 ## Step 1 — Install LeRobot
 Follow the full [Installation Guide](./installation) for environment setup, then add the SO-101 motor stack and log in to the Hub:
 ```bash
 pip install 'lerobot[feetech]'
 git lfs install && git lfs pull
 hf auth login                 # paste a token from https://huggingface.co/settings/tokens
 ```
 Sanity check — the CLI entry points should be available:
 ```bash
 lerobot-find-port --help
 ```
 ---
 ## Step 2 — Identify USB ports and motor IDs
 Plug **only the follower arm** in (USB + power) and run:
 ```bash
 lerobot-find-port
 ```
 When prompted, unplug it and press Enter. Note the printed port — that's your `<FOLLOWER_PORT>`. Repeat with only the **leader arm** plugged in to get `<LEADER_PORT>`.
 > [!TIP]
 > On Linux, USB ports look like `/dev/ttyACM0`; on macOS like `/dev/tty.usbmodem...`. On Linux you may need `sudo chmod 666 /dev/ttyACM0` to grant access.
 If your motors are brand-new (or repurposed), set their IDs and baudrate **once per arm**:
 ```bash
 lerobot-setup-motors --robot.type=so101_follower --robot.port=<FOLLOWER_PORT>
 lerobot-setup-motors --teleop.type=so101_leader  --teleop.port=<LEADER_PORT>
 ```
 The script walks you through connecting motors one at a time. Full details: [SO-101 → Configure the motors](./so101#configure-the-motors).
 ---
 ## Step 3 — Calibrate
 Center every joint roughly in the middle of its range, then run:
 ```bash
 lerobot-calibrate \
    --robot.type=so101_follower \
    --robot.port=<FOLLOWER_PORT> \
    --robot.id=my_follower
 lerobot-calibrate \
    --teleop.type=so101_leader \
    --teleop.port=<LEADER_PORT> \
    --teleop.id=my_leader
 ```
 After pressing Enter, sweep each joint through its full range of motion, then press Enter again to finish.
 > [!WARNING]
 > The `--robot.id` / `--teleop.id` values (`my_follower`, `my_leader`) become the **calibration keys**. Reuse the same IDs in every later command — that's how LeRobot finds the calibration on disk.
 Watch the [calibration video](./so101#calibrate) if anything is unclear.
 ---
 ## Step 4 — Teleoperate (sanity check, no recording)
 Before recording anything, confirm the leader drives the follower correctly:
 ```bash
 lerobot-teleoperate \
    --robot.type=so101_follower \
    --robot.port=<FOLLOWER_PORT> \
    --robot.id=my_follower \
    --robot.cameras="{ top: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30} }" \
    --teleop.type=so101_leader \
    --teleop.port=<LEADER_PORT> \
    --teleop.id=my_leader \
    --display_data=true
 ```
 A Rerun window should open showing the camera feed and joint angles. Move the leader — the follower should mirror it in real time. If it doesn't, see [Troubleshooting & FAQ](./troubleshooting).
 Don't know which camera index is which? Run `lerobot-find-cameras` — it saves a frame from each detected camera so you can pick the right one.
 ---
 ## Step 5 — Record a dataset (30 episodes)
 Now record demonstrations. Pick a short, repeatable task (e.g. *"put the red brick in the bowl"*). The dataset is pushed to the Hub under your username:
 ```bash
 export HF_USER=<your-hf-username>
 lerobot-record \
    --robot.type=so101_follower \
    --robot.port=<FOLLOWER_PORT> \
    --robot.id=my_follower \
    --robot.cameras="{ top: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, wrist: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30} }" \
    --teleop.type=so101_leader \
    --teleop.port=<LEADER_PORT> \
    --teleop.id=my_leader \
    --dataset.repo_id=${HF_USER}/so101_quickstart \
    --dataset.num_episodes=30 \
    --dataset.single_task="Put the red brick in the bowl" \
    --dataset.streaming_encoding=true \
    --display_data=true
 ```
 **Keyboard controls during recording:**
 - **`→` (Right Arrow)** — save the current episode and move to the next.
 - **`←` (Left Arrow)** — discard the current episode and retry.
 - **`Esc`** — stop, encode videos, and upload to the Hub.
 > [!TIP]
 > **Quality beats quantity.** 30 clean, varied episodes (different brick positions, lighting, camera shake) train a much better policy than 100 identical ones. Move the object around. Vary your speed slightly.
 When you're done, your dataset lives at `https://huggingface.co/datasets/${HF_USER}/so101_quickstart`. You can preview it in the browser. For deeper recording options (resume, multiple tasks, custom processors), see [Imitation learning end-to-end → Record](./il_robots#record-a-dataset).
 ---
 ## Step 6 — Train ACT
 ACT (Action Chunking Transformer) is the right default for a first run — small, fast, and works well on 30 episodes.
 ```bash
 lerobot-train \
    --dataset.repo_id=${HF_USER}/so101_quickstart \
    --policy.type=act \
    --output_dir=outputs/train/act_so101_quickstart \
    --job_name=act_so101_quickstart \
    --policy.device=cuda \
    --policy.repo_id=${HF_USER}/act_so101_quickstart \
    --steps=20000 \
    --wandb.enable=true
 ```
 A few notes:
 - Replace `--policy.device=cuda` with `mps` on Apple Silicon, or `cpu` if you have no GPU (very slow — not recommended for a real run).
 - `--wandb.enable=true` is optional. If you use it, run `wandb login` first. Otherwise drop the flag.
 - Checkpoints land in `outputs/train/act_so101_quickstart/checkpoints/`. The final model is also pushed to the Hub at the `--policy.repo_id` you specified.
 - To resume from an interruption: `lerobot-train --config_path=outputs/train/act_so101_quickstart/checkpoints/last/pretrained_model/train_config.json --resume=true`.
 > [!TIP]
 > **No GPU locally?** Train on Google Colab using the [ACT notebook](./notebooks#training-act), or rent a GPU via [Hugging Face Jobs](./il_robots#train-using-hugging-face-jobs) — pay-as-you-go, no setup.
 For why ACT is the default and when to switch to SmolVLA, Pi0, or another policy, see [Choosing a policy](./policies_overview).
 ---
 ## Step 7 — Run your policy on the robot
 Deploy with `lerobot-rollout`. **Use the same camera layout you used while recording** — keys and resolutions must match.
 ```bash
 lerobot-rollout \
    --strategy.type=base \
    --policy.path=${HF_USER}/act_so101_quickstart \
    --robot.type=so101_follower \
    --robot.port=<FOLLOWER_PORT> \
    --robot.id=my_follower \
    --robot.cameras="{ top: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, wrist: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30} }" \
    --task="Put the red brick in the bowl" \
    --duration=60
 ```
 `--duration` is in seconds — leave it off to run until you stop the script. You should see the follower arm move on its own, attempting the task.
 If observations from the robot use different keys than the policy expects, you'll need a [rename map](./rename_map). If latency matters, look at [async inference](./async) and [real-time chunking](./rtc).
 ---
 ## You're done 🎉
 You now have a working IL pipeline end-to-end. From here, the natural next steps are:
 - **Improve the policy** — record more diverse episodes, train longer, or try a stronger model. See [Choosing a policy](./policies_overview).
 - **Go deeper on imitation learning** — [Imitation learning end-to-end](./il_robots) covers multi-camera setups, multi-task datasets, episode replay, evaluation, and Hugging Face Jobs.
 - **Try RL with a human in the loop** — [HIL-SERL](./hilserl) trains a policy that improves while you correct it.
 - **Use a different robot** — see [Supported robots](./so101) for low-cost arms, mobile platforms, bimanual, and humanoid.
 - **Build something new** — [Bring your own hardware](./integrate_hardware) and [Add a new policy](./bring_your_own_policies).
 Stuck on something? Check [Troubleshooting & FAQ](./troubleshooting), or ask on [Discord](https://discord.gg/s3KuuzsPFb).
@@ -255,6 +255,7 @@ def extract_path_fields_from_config(config_path: str, path_fields: list[str]) ->
            remaining = config_data[field]
            if remaining:
                _config_yaml_overrides[field] = _flatten_to_cli_args(remaining)
            else:
                del config_data[field]
            modified = True
@@ -310,13 +311,7 @@ def wrap(config_path: Path | None = None) -> Callable[[F], F]:
                    cli_args = filter_arg("config_path", cli_args)
                    cfg = argtype.from_pretrained(config_path_cli, cli_args=cli_args)
                else:
-                    if config_path_cli:
+                    cfg = draccus.parse(config_class=argtype, config_path=config_path, args=cli_args)
                        cli_args = filter_arg("config_path", cli_args)
                    cfg = draccus.parse(
                        config_class=argtype,
                        config_path=config_path_cli or config_path,
                        args=cli_args,
                    )
            response = fn(cfg, *args, **kwargs)
            return response
@@ -60,7 +60,6 @@ class Eagle25VLPreTrainedModel(PreTrainedModel):
        "SiglipEncoderLayer",
    ]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn = True
    _supports_flash_attn_2 = True
    _supports_cache_class = True
    _supports_static_cache = True
@@ -124,6 +124,7 @@ class Eagle25VLProcessor(ProcessorMixin):
        "videos_kwargs",
        "text_kwargs",
    ]
    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"
    def __init__(
@@ -206,11 +206,7 @@ def _build_eagle_processor(tokenizer_assets_repo: str = DEFAULT_TOKENIZER_ASSETS
            "Vendor files are copied during model creation. Create the policy/model first, "
            "or call ensure_eagle_cache_ready() before building processors."
        )
-    proc = AutoProcessor.from_pretrained(
+    proc = AutoProcessor.from_pretrained(str(cache_dir), trust_remote_code=True, use_fast=True)
        str(cache_dir),
        trust_remote_code=True,
        fix_mistral_regex=False,
    )
    proc.tokenizer.padding_side = "left"
    return proc
@@ -1,14 +1,10 @@
 """Tests for policy.path support in YAML config files (issue #2957)."""
 import json
 import sys
 import tempfile
 from dataclasses import dataclass, field
 from unittest.mock import patch
 import yaml
 from lerobot.configs import parser
 from lerobot.configs.parser import (
    _config_path_args,
    _config_yaml_overrides,
@@ -20,8 +16,7 @@ from lerobot.configs.parser import (
 def test_extract_path_fields_from_yaml():
-    """Test that policy.path is extracted from a YAML config and the policy block
+    """Test that policy.path is extracted from a YAML config and removed."""
    is removed entirely (siblings are captured separately as cli_overrides)."""
    config = {
        "dataset": {"repo_id": "lerobot/pusht"},
        "policy": {"type": "smolvla", "path": "lerobot/smolvla_base", "push_to_hub": False},
@@ -31,33 +26,26 @@ def test_extract_path_fields_from_yaml():
        config_path = f.name
    _config_path_args.clear()
    _config_yaml_overrides.clear()
    cleaned_path = extract_path_fields_from_config(config_path, ["policy"])
    # Path should be extracted and stored
    assert _config_path_args["policy"] == "lerobot/smolvla_base"
-    # Cleaned config should not have the policy block at all -- draccus must not
+    # Cleaned config should not have the path field
    # try to decode it as PreTrainedConfig; the actual config comes from
    # from_pretrained(path) with the captured overrides applied on top.
    with open(cleaned_path) as f:
        cleaned = yaml.safe_load(f)
-    assert "policy" not in cleaned
+    assert "path" not in cleaned["policy"]
    assert cleaned["policy"]["type"] == "smolvla"
    assert cleaned["policy"]["push_to_hub"] is False
    # Original dataset should be untouched
    assert cleaned["dataset"]["repo_id"] == "lerobot/pusht"
    # Sibling overrides (excluding type/path) captured for from_pretrained.
    overrides = get_yaml_overrides("policy")
    assert any("push_to_hub=false" in o for o in overrides)
    _config_path_args.clear()
    _config_yaml_overrides.clear()
 def test_extract_path_fields_from_json():
-    """Test that policy.path is extracted from a JSON config and the policy
+    """Test that policy.path is extracted from a JSON config."""
    block is removed entirely."""
    config = {
        "policy": {"type": "act", "path": "some/local/path"},
    }
@@ -66,17 +54,15 @@ def test_extract_path_fields_from_json():
        config_path = f.name
    _config_path_args.clear()
    _config_yaml_overrides.clear()
    cleaned_path = extract_path_fields_from_config(config_path, ["policy"])
    assert _config_path_args["policy"] == "some/local/path"
    with open(cleaned_path) as f:
        cleaned = json.load(f)
-    assert "policy" not in cleaned
+    assert "path" not in cleaned["policy"]
    _config_path_args.clear()
    _config_yaml_overrides.clear()
 def test_extract_no_path_returns_original():
@@ -230,91 +216,3 @@ def test_flatten_nested_with_bools():
    args = _flatten_to_cli_args(d)
    assert "--optimizer.use_warmup=true" in args
    assert "--optimizer.lr=0.01" in args
 def test_extract_removes_field_with_siblings_and_no_type():
    """Regression: when policy.path has siblings but no type:, the entire policy
    block must still be removed from the cleaned config. Otherwise draccus tries
    to decode the leftover dict as PreTrainedConfig and crashes on the missing
    type discriminator.
    """
    config = {
        "dataset": {"repo_id": "lerobot/pusht"},
        "policy": {
            "path": "lerobot/smolvla_base",
            "n_action_steps": 10,
            "dtype": "bfloat16",
        },
    }
    with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
        yaml.dump(config, f)
        config_path = f.name
    _config_path_args.clear()
    _config_yaml_overrides.clear()
    cleaned_path = extract_path_fields_from_config(config_path, ["policy"])
    with open(cleaned_path) as f:
        cleaned = yaml.safe_load(f) or {}
    assert "policy" not in cleaned, "policy block should be fully removed when path is present"
    assert cleaned["dataset"]["repo_id"] == "lerobot/pusht"
    assert _config_path_args["policy"] == "lerobot/smolvla_base"
    overrides = get_yaml_overrides("policy")
    assert any("n_action_steps=10" in o for o in overrides)
    assert any("dtype=bfloat16" in o for o in overrides)
    _config_path_args.clear()
    _config_yaml_overrides.clear()
@dataclass
 class _DummyNested:
    foo: int = 0
@dataclass
 class _DummyConfig:
    nested: _DummyNested = field(default_factory=_DummyNested)
    other: str = "default"
    @classmethod
    def __get_path_fields__(cls):
        return ["nested"]
 def test_wrap_uses_cleaned_config_for_draccus_parse():
    """Regression: wrap() updates config_path_cli to point at the cleaned temp
    file but must propagate that to the draccus.parse fallback branch. Without
    the fix, cli_args still contains --config_path=<original> and draccus reads
    the original YAML with `path:` still in it, crashing on the unknown field.
    """
    config = {
        "nested": {"path": "some/checkpoint", "foo": 42},
        "other": "set-via-yaml",
    }
    with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
        yaml.dump(config, f)
        config_path = f.name
    _config_path_args.clear()
    _config_yaml_overrides.clear()
    captured: dict = {}
    @parser.wrap()
    def main(cfg: _DummyConfig) -> _DummyConfig:
        captured["cfg"] = cfg
        return cfg
    with patch.object(sys, "argv", ["prog", f"--config_path={config_path}"]):
        main()
    assert captured["cfg"].other == "set-via-yaml"
    assert _config_path_args["nested"] == "some/checkpoint"
    # Cleaned config dropped `nested:` entirely; defaults stand for this wrapper
    # class (a real PreTrainedConfig would now load the checkpoint and apply
    # the captured yaml_overrides via from_pretrained()).
    assert captured["cfg"].nested.foo == 0
    _config_path_args.clear()
    _config_yaml_overrides.clear()
Author	SHA1	Message	Date
Nikodem Bartnik	a24d10f5bb	add quick AI draft for quickstart	2026-05-26 13:10:24 +02:00
Nikodem Bartnik	32279544ea	add new docs chapters structure	2026-05-26 12:01:33 +02:00