chore: enable Dependabot weekly GitHub Actions bumps

2026-05-27 22:49:48 +00:00 · 2026-05-26 10:32:49 +00:00
4 changed files with 140 additions and 562 deletions
@@ -0,0 +1,11 @@
 version: 2
 updates:
  - package-ecosystem: "github-actions"
    directory: "/"
    schedule:
      interval: "weekly"
    cooldown:
      default-days: 7
    groups:
      actions:
        patterns: ["*"]
@@ -1,172 +0,0 @@
 - 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"
@@ -1,214 +1,172 @@
 # 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: Command cheat sheet
+    title: Cheat sheet
  title: Get started
 - sections:
  - local: il_robots
-    title: Imitation learning end-to-end
+    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
+    title: Human In the Loop Data Collection
-  - local: inference
+  - local: peft_training
-    title: Deploying a trained policy
+    title: Training with PEFT (e.g., LoRA)
  - local: rename_map
-    title: Matching dataset keys to a policy (rename map)
+    title: Using Rename Map and Empty Cameras
-  title: Your first project
+  title: "Tutorials"
 - sections:
  - local: hardware_guide
-    title: Compute hardware guide
+    title: Compute Hardware Guide
  - local: torch_accelerators
    title: PyTorch accelerators
-  - local: multi_gpu_training
+  title: "Compute & Hardware"
    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: Dataset tools
+    title: Using the Dataset Tools
  - local: language_and_recipes
-    title: Language columns & recipes
+    title: Language Columns and Recipes
  - local: tools
-    title: Tool calls in datasets
+    title: Tools
  - local: video_encoding_parameters
    title: Video encoding parameters
  - local: streaming_video_encoding
-    title: Streaming video encoding
+    title: Streaming Video Encoding
-  - local: porting_datasets_v3
+  title: "Datasets"
    title: Porting datasets to v3
  title: Datasets
 - sections:
-  - local: policies_overview    # NEW (to create) — concept hub + "choose a policy" decision guide
+  - local: act
-    title: Choosing a policy
+    title: ACT
-  - sections:
+  - local: smolvla
-    - local: act
+    title: SmolVLA
-      title: ACT
+  - local: pi0
-    - local: smolvla
+    title: π₀ (Pi0)
-      title: SmolVLA
+  - local: pi0fast
-    - local: pi0
+    title: π₀-FAST (Pi0Fast)
-      title: π₀ (Pi0)
+  - local: pi05
-    - local: pi0fast
+    title: π₀.₅ (Pi05)
-      title: π₀-FAST
+  - local: eo1
-    - local: pi05
+    title: EO-1
-      title: π₀.₅ (Pi05)
+  - local: groot
-    - local: eo1
+    title: NVIDIA GR00T N1.5
-      title: EO-1
+  - local: xvla
-    - local: groot
+    title: X-VLA
-      title: NVIDIA GR00T N1.5
+  - local: multi_task_dit
-    - local: xvla
+    title: Multitask DiT Policy
-      title: X-VLA
+  - local: walloss
-    - local: walloss
+    title: WALL-OSS
-      title: WALL-OSS
+  title: "Policies"
    - local: multi_task_dit
      title: Multitask DiT
    title: Policy reference
  title: Policies
 - sections:
  - local: async
    title: Async inference
  - local: rtc
    title: Real-time chunking (RTC)
  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: SARM
-  title: Reinforcement learning
+  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: LeIsaac — control & train in sim
+    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
+    title: NVIDIA IsaacLab Arena Environments
-  - sections:
+  - local: vlabench
-    - local: libero
+    title: VLABench
-      title: LIBERO
+  title: "Benchmarks"
    - 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: vlabench
      title: VLABench
    title: Benchmark suites
  title: Simulation & benchmarks
 - sections:
  - local: introduction_processors
-    title: Introduction to processors
+    title: Introduction to Robot Processors
  - local: processors_robots_teleop
    title: Processors for robots & teleoperators
  - local: env_processor
    title: Environment processors
  - local: action_representations
    title: Action representations
  - local: debug_processor_pipeline
-    title: Debugging a pipeline
+    title: Debug your processor pipeline
  - local: implement_your_own_processor
-    title: Implementing your own processor
+    title: Implement your own processor
-  title: Processors
+  - 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:
-  - sections:
+  - local: so101
-    - local: so101
+    title: SO-101
-      title: SO-101
+  - local: so100
-    - local: so100
+    title: SO-100
-      title: SO-100
+  - local: koch
-    - local: koch
+    title: Koch v1.1
-      title: Koch v1.1
+  - local: lekiwi
-    - local: omx
+    title: LeKiwi
-      title: OMX
+  - local: hope_jr
-    - local: openarm
+    title: Hope Jr
-      title: OpenArm
+  - local: reachy2
-    title: Low-cost arms
+    title: Reachy 2
-  - sections:
+  - local: unitree_g1
-    - local: lekiwi
+    title: Unitree G1
-      title: LeKiwi
+  - local: earthrover_mini_plus
-    - local: earthrover_mini_plus
+    title: Earth Rover Mini
-      title: Earth Rover Mini
+  - local: omx
-    title: Mobile platforms
+    title: OMX
-  - sections:
+  - local: openarm
-    - local: hope_jr
+    title: OpenArm
-      title: Hope Jr
+  - local: rebot_b601
-    - local: reachy2
+    title: reBot B601-DM
-      title: Reachy 2
+  title: "Robots"
-    - local: unitree_g1
+- sections:
-      title: Unitree G1
+  - local: phone_teleop
-    title: Bimanual & humanoid
+    title: Phone
-  - sections:
+  title: "Teleoperators"
    - local: rebot_b601
      title: reBot B601-DM
    title: Research & industrial
  title: Supported robots
 - sections:
  - local: cameras
    title: Cameras
-  - local: phone_teleop
+  title: "Sensors"
    title: Phone teleoperation
  - local: feetech
    title: Feetech firmware update
  - local: damiao
    title: Damiao motors & CAN bus
  title: Sensors, teleop & motors
 - sections:
  - local: integrate_hardware
    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
+    title: Notebooks
-  - local: backwardcomp
+  - local: feetech
-    title: Backward compatibility
+    title: Updating Feetech Firmware
-  title: Reference
+  - local: damiao
-
+    title: Damiao Motors and CAN Bus
  title: "Resources"
 - sections:
  - local: contributing
-    title: Contributing to LeRobot
+    title: Contribute to LeRobot
-  title: About
+  - local: backwardcomp
    title: Backward compatibility
  title: "About"
@@ -1,219 +0,0 @@
 # 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).