feat(upgrade): adding new robots, teleoperators and sensors to the third party packages list

docs(third party): adding a small list of noticeable third party robots packages
Update GR00T 1.7 LIBERO checkpoints (#3961 )
2026-07-26 03:06:01 +00:00 · 2026-07-16 14:56:32 +02:00 · 2026-07-12 23:36:38 +02:00 · 2026-07-08 13:25:54 +02:00 · 2026-07-08 07:31:10 +02:00 · 2026-07-06 12:52:39 +02:00
27 changed files with 3148 additions and 72 deletions
@@ -55,7 +55,7 @@ jobs:
      github.repository == 'huggingface/lerobot'
    permissions:
      contents: read
-    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
+    uses: huggingface/doc-builder/.github/workflows/build_main_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # main
    with:
      commit_sha: ${{ github.sha }}
      package: lerobot
@@ -78,7 +78,7 @@ jobs:
    permissions:
      contents: read
      pull-requests: write
-    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@2430c1ec91d04667414e2fa31ecfc36c153ea391  # main
+    uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@e60a538eea9817ab312196d0d233604b01697265  # main
    with:
      commit_sha: ${{ github.event.pull_request.head.sha }}
      pr_number: ${{ github.event.number }}
@@ -87,7 +87,7 @@ Learn more about it in the [LeRobotDataset Documentation](https://huggingface.co

 ## SoTA Models

-LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, and Vision-Language-Action (VLA) models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.
+LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, Vision-Language-Action (VLA) models, World Models, and Reward Models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.

 <p align="center">
  <img alt="Gr00t Architecture" src="./media/readme/VLA_architecture.jpg" width="640px">
@@ -101,13 +101,13 @@ lerobot-train \
  --dataset.repo_id=lerobot/aloha_mobile_cabinet
 ```

-| Category                   | Models                                                                                                                                                                                                                                                                                                                                                     |
-| -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md), [Multitask DiT Policy](./docs/source/policy_multi_task_dit_README.md)                                                                                                                                    |
-| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                                                                                                                                                                                                                |
-| **VLAs Models**            | [Pi0](./docs/source/pi0.mdx), [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.7](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx), [EO-1](./docs/source/eo1.mdx), [MolmoAct2](./docs/source/molmoact2.mdx), [WALL-OSS](./docs/source/walloss.mdx) |
-| **World Models**           | [VLA-JEPA](./docs/source/vla_jepa.mdx) (more coming soon)                                                                                                                                                                                                                                                                                                  |
-| **Reward Models**          | [SARM](./docs/source/sarm.mdx), [TOPReward](./docs/source/topreward.mdx), [Robometer](./docs/source/robometer.mdx)                                                                                                                                                                                                                                         |
+| Category                   | Models                                                                                                                                                                                                                                                                                                                                                                                     |
+| -------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| **Imitation Learning**     | [ACT](./docs/source/policy_act_README.md), [Diffusion](./docs/source/policy_diffusion_README.md), [VQ-BeT](./docs/source/policy_vqbet_README.md), [Multitask DiT Policy](./docs/source/policy_multi_task_dit_README.md)                                                                                                                                                                    |
+| **Reinforcement Learning** | [HIL-SERL](./docs/source/hilserl.mdx), [TDMPC](./docs/source/policy_tdmpc_README.md) & QC-FQL (coming soon)                                                                                                                                                                                                                                                                                |
+| **VLAs Models**            | [Pi0](./docs/source/pi0.mdx), [Pi0Fast](./docs/source/pi0fast.mdx), [Pi0.5](./docs/source/pi05.mdx), [GR00T N1.7](./docs/source/policy_groot_README.md), [SmolVLA](./docs/source/policy_smolvla_README.md), [XVLA](./docs/source/xvla.mdx), [EO-1](./docs/source/eo1.mdx), [MolmoAct2](./docs/source/molmoact2.mdx), [WALL-OSS](./docs/source/walloss.mdx), [EVO1](./docs/source/evo1.mdx) |
+| **World Models**           | [VLA-JEPA](./docs/source/vla_jepa.mdx), [LingBot-VA](./docs/source/lingbot_va.mdx), [FastWAM](./docs/source/fastwam.mdx)                                                                                                                                                                                                                                                                   |
+| **Reward Models**          | [SARM](./docs/source/sarm.mdx), [TOPReward](./docs/source/topreward.mdx), [Robometer](./docs/source/robometer.mdx)                                                                                                                                                                                                                                                                         |

 Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub

@@ -165,14 +165,24 @@
    title: OpenArm
  - local: rebot_b601
    title: reBot B601-DM
+  - local: third_party_robots
+<<<<<<< Updated upstream
+    title: Third-Party Robots Packages
+=======
+    title: Third-Party Robots & Teleoperators
+>>>>>>> Stashed changes
  title: "Robots"
 - sections:
  - local: phone_teleop
    title: Phone
+  - local: isaac_teleop
+    title: Isaac Teleop
  title: "Teleoperators"
 - sections:
  - local: cameras
    title: Cameras
+  - local: third_party_sensors
+    title: Third-Party Cameras & Sensors
  title: "Sensors"
 - sections:
  - local: notebooks
@@ -295,11 +295,12 @@ The file names are load-bearing: the factory does lazy imports by name, and the

 ### Wiring

-Three places need to know about your policy. All by name.
+Four places need to know about your policy. All by name.

 1. **`policies/__init__.py`** — re-export `MyPolicyConfig` and add it to `__all__`. **Don't** re-export the modeling class; it loads lazily through the factory (so `import lerobot` stays fast).
 2. **`factory.py:get_policy_class`** — add a branch returning `MyPolicy` from a lazy import.
 3. **`factory.py:make_policy_config`** and **`factory.py:make_pre_post_processors`** — same idea, two more branches.
+4. **`templates/lerobot_modelcard_template.md` and the root `README.md`** — the template is what `push_model_to_hub` renders into the model card of every checkpoint trained with your policy: add a one-line description of your policy in the `model_name` branches, map it in `policy_docs` so cards link to your MDX guide, and optionally add an architecture image to `diagrams`. Then add your policy to the models table in the root `README.md`, under the right category, linking to your doc page.

 Mirror an existing policy that's structurally similar to yours; the diff is small.

@@ -371,6 +372,8 @@ The general expectations are in [`CONTRIBUTING.md`](https://github.com/huggingfa
 - [ ] Optional deps live behind a `[project.optional-dependencies]` extra and the `TYPE_CHECKING + require_package` guard.
 - [ ] `tests/policies/` updated; backward-compat artifact committed & policy-specific tests.
 - [ ] `src/lerobot/policies/<name>/README.md` symlinked into `docs/source/policy_<name>_README.md`; user-facing `docs/source/<name>.mdx` written and added to `_toctree.yml`.
+- [ ] `templates/lerobot_modelcard_template.md` has a description entry and a `policy_docs` link for your policy.
+- [ ] The models table in the root `README.md` lists your policy in the right category, linking to your doc page.
 - [ ] At least one reproducible benchmark eval in the policy MDX with a published checkpoint (sim benchmark, or real-robot dataset + checkpoint).

 The fastest way to get a clean PR is to copy the directory of the existing policy closest to yours, rename, and replace contents method by method. Don't wait until everything is polished — open a draft PR early and iterate with us; reviewers would much rather give feedback on a half-finished branch than a fully-merged one.
@@ -160,13 +160,13 @@ This will follow the recipe found [here](https://github.com/NVIDIA/Isaac-GR00T/b

 Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite):

-| Suite            | Success rate |
-| ---------------- | -----------: |
-| LIBERO Spatial   |          94% |
-| LIBERO Object    |          98% |
-| LIBERO Goal      |          93% |
-| LIBERO 10 (Long) |          90% |
-| **Average**      |   **93.75%** |
+| Suite            | Success rate | Checkpoint                                                                                                    |
+| ---------------- | -----------: | ------------------------------------------------------------------------------------------------------------- |
+| LIBERO Spatial   |          95% | [nvidia/gr00t17-lerobot-libero_spatial-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_spatial-640) |
+| LIBERO Object    |         100% | [nvidia/gr00t17-lerobot-libero_object-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_object-640)   |
+| LIBERO Goal      |          98% | [nvidia/gr00t17-lerobot-libero_goal-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_goal-640)       |
+| LIBERO 10 (Long) |          93% | [nvidia/gr00t17-lerobot-libero_10-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_10-640)           |
+| **Average**      |    **96.5%** |                                                                                                               |

 ```bash
 export MODEL_ID=your_trained_model_on_huggingface
@@ -0,0 +1,397 @@
+# Isaac Teleop
+
+Control your robot with NVIDIA [Isaac Teleop](https://github.com/NVIDIA/IsaacTeleop), a
+multi-modal teleoperation framework. Isaac Teleop drives a single `TeleopSession` from a range
+of input devices — XR (VR) controllers, hand tracking, full-body tracking, Manus gloves, foot
+pedals, and more.
+
+In LeRobot, Isaac Teleop ships as a self-contained example under
+[`examples/isaac_teleop_to_so101/`](https://github.com/huggingface/lerobot/tree/main/examples/isaac_teleop_to_so101).
+Each Isaac Teleop input device is its own `Teleoperator` subclass in the example's
+`isaac_teleop` package, sharing one session lifecycle (see `IsaacTeleopTeleoperator`). The
+devices available today are the **XR controller** (`XRController`) and a back-drivable
+**SO-101 leader arm** (`SO101LeaderArm`); Manus gloves and hand/full-body tracking are the
+natural next devices. This guide focuses on the XR controller; the SO-101 leader is summarized
+under [Run the example](#step-3-run-the-example).
+
+**In this guide you'll learn:**
+
+- How an Isaac Teleop device drives a robot end‑effector (EE) target
+- How the _clutch_ (squeeze/grip on the XR controller) engages teleoperation without jerking the arm
+- How to run the SO‑101 teleoperation example and tune motion / gripper / IK
+
+## Installation
+
+The example lives in the LeRobot repository (it is not part of the `lerobot` pip package), so
+clone the repo and install from source. The canonical, always-up-to-date install and usage
+reference is the example's
+[`README.md`](https://github.com/huggingface/lerobot/tree/main/examples/isaac_teleop_to_so101/README.md);
+in short:
+
+```bash
+git clone https://github.com/huggingface/lerobot.git
+cd lerobot
+uv pip install -e ".[feetech,kinematics,dataset]" "huggingface_hub>=1.5"
+uv pip install "isaacteleop[cloudxr,retargeters-lite]~=1.3.131" "scipy>=1.14"
+```
+
+`isaacteleop` is published on public PyPI (Linux only). The `cloudxr` extra brings the CloudXR
+runtime bindings; `retargeters-lite` is the scipy-based retargeter path that resolves on both
+x86_64 and ARM (on aarch64 — e.g. a DGX Spark — the full `retargeters` extra does not resolve
+because of its `dex-retargeting`/`nlopt` pins, which is why it is not the default here). On
+x86_64 you can additionally install the full retargeter stack:
+
+```bash
+uv pip install "isaacteleop[retargeters]~=1.3.131"
+```
+
+### Set up CloudXR and connect a headset
+
+Isaac Teleop streams the headset to your machine over **NVIDIA CloudXR**, which provides the
+OpenXR runtime the session connects to. By default LeTeleop **auto-launches the CloudXR runtime
+for you** when you call `teleop_device.connect()` — you no longer have to run `python -m
+isaacteleop.cloudxr` and `source cloudxr.env` in a separate shell. All you need is a supported
+headset connected and the CloudXR firewall ports open. Follow the Isaac Teleop
+[Quick Start](https://nvidia.github.io/IsaacTeleop/main/getting_started/quick_start.html) for the
+headset-pairing and firewall details.
+
+**First run (EULA).** The very first launch must accept the NVIDIA CloudXR EULA. The auto-launch
+prompts for it **on stdin**, so on a headless machine it will hang waiting for input. Bootstrap
+the EULA once, interactively, with:
+
+```bash
+python -m isaacteleop.cloudxr --accept-eula   # one-time: accept the CloudXR EULA
+```
+
+After that, `connect()` launches the runtime non-interactively. The launch **blocks for ~30s**
+while the runtime comes up.
+
+**Configuration.** Two fields on `IsaacTeleopConfig` (shared by every device) control this:
+
+- `auto_launch_cloudxr` (default `True`) — whether `connect()` starts the runtime. Set `False`
+  when CloudXR is already running externally.
+- `cloudxr_env_file` (default `None`) — an optional CloudXR device-profile `.env` selecting the
+  headset transport (e.g. an Apple Vision Pro profile). This is launcher **input**; it is not the
+  `~/.cloudxr/run/cloudxr.env` **output** file the old manual flow told you to `source`. `None`
+  keeps the default auto-WebRTC profile — though the SO-101 example overrides it to the
+  `default.env` shipped next to `teleoperate.py` unless you pass `--teleop.cloudxr_env_file`.
+
+**Opting out.** To skip the auto-launch (CloudXR already running), either set
+`auto_launch_cloudxr=False` or export:
+
+```bash
+export LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1
+```
+
+The **env var takes precedence over the config field**: if `LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1` is
+set, the auto-launch is skipped even when `auto_launch_cloudxr=True`. This variable is
+**independent** of Isaac Lab's `ISAACLAB_CXR_SKIP_AUTOLAUNCH` — setting one does not affect the
+other.
+
+**One teleoperator per process.** The CloudXR runtime configures the environment process-wide (a
+singleton), so run a single Isaac Teleop teleoperator per process.
+
+**Shutting down.** Always call `teleop_device.disconnect()` on exit — including on Ctrl-C. Wrap
+your teleoperation loop in `try/finally` and call `disconnect()` in the `finally`. This tears down
+the OpenXR session **before** the CloudXR runtime, which is the required order; the launcher's
+`atexit` hook only reaps the runtime and does not run the session's `__exit__`, so without an
+explicit `disconnect()` an interrupted run shuts down in the wrong order.
+
+```python
+teleop_device.connect()
+try:
+    while True:
+        action = teleop_device.get_action()
+        # ... drive the robot ...
+finally:
+    teleop_device.disconnect()
+```
+
+See [System Requirements](https://nvidia.github.io/IsaacTeleop/main/references/requirements.html)
+for supported OS / GPU / CloudXR versions and headsets.
+
+## How it works
+
+The XR controller is one Isaac Teleop **input** device. `XRController` is a deliberately thin
+reader: it exposes the **raw** controller grip pose — already statically rebased into the robot
+base frame — plus the squeeze and trigger analog values. It has **no** retargeters and **no**
+clutch logic of its own. The clutch (engage latch + delta rebasing onto the EE) and the gripper
+mapping live downstream in the example loop, which then feeds LeRobot's existing closed‑loop
+Cartesian IK pipeline — the same one the phone teleoperator uses. The device‑specific pieces are
+`XRController`, the loop's `Clutch`, and `MapXRControllerActionToRobotAction`; everything downstream
+(`EEBoundsAndSafety`, `InverseKinematicsEEToJoints`) is shared, and a future device (e.g. Manus
+gloves) would swap in its own `teleop_<device>.py` + processor while reusing the rest.
+
+`XRController._build_pipeline` wires Isaac Teleop's `ControllersSource` — statically rebased into
+the robot base frame by the native `ControllerTransform` (`base_T_anchor`) — and exposes the
+transformed controller stream verbatim. `get_action()` reads the grip pose, squeeze, and trigger
+straight off it; the session is always stepped `RUNNING` (there is no clutch retargeter to gate).
+
+The `Clutch` class (in `examples/isaac_teleop_to_so101/isaac_teleop/clutch.py`, driven by the
+loop in `common.py`) mirrors Isaac Teleop's `SO101ClutchRetargeter`, but lives in-loop so the
+device can stay a thin reader:
+
+- It latches its engage origin on the squeeze **engage edge** (the frame the squeeze first crosses
+  `clutch_threshold`) and rebases both position and orientation around it, so engaging does not
+  teleport the arm. `Clutch.rebase` returns the absolute base-frame target as a `(pos, quat)`
+  pair, which the loop concatenates into the 7D `ee_pose` fed to the processor.
+- The analog trigger becomes a gripper `closedness` in `[0, 1]` (0 = open, 1 = closed),
+  proportional to the trigger pull, which `MapXRControllerActionToRobotAction` maps to a jaw target.
+
+See the Isaac Teleop
+[Retargeting interface](https://nvidia.github.io/IsaacTeleop/main/references/retargeting/index.html)
+and [architecture overview](https://nvidia.github.io/IsaacTeleop/main/overview/architecture.html)
+for how source nodes and retargeters compose.
+
+```text
+  VR controller (OpenXR)
+        │
+        ▼
+  XRController.get_action()          ── raw base-frame grip_pos / grip_quat + squeeze + trigger
+        │                                (TeleopSession always stepped RUNNING; clutch lives downstream)
+        ▼
+  Clutch.rebase(grip_pos, grip_quat) ── engage-relative delta applied to the EE home (pos + orient)
+        │  ee_pose (7) / closedness       → absolute ee_pose; closedness = trigger
+        ▼
+  MapXRControllerActionToRobotAction ── absolute ee.x/y/z; ee.w* = orientation rotvec target;
+        │  ee.x/y/z / ee.w* / ee.gripper_pos   ee.gripper_pos = (1 - closedness) * 100
+        ▼
+  EEBoundsAndSafety                ── workspace clip + per-frame step clamp (clamp+warn)
+        │
+        ▼
+  InverseKinematicsEEToJoints      ── closed-loop Placo IK; position + soft-orientation
+        │  (orientation_weight=0.01)   (passes ee.gripper_pos → gripper.pos)
+        ▼
+  SO-101 follower joint targets
+```
+
+### The clutch: owned by the example loop
+
+Unlike the phone pipeline (which splits the clutch across `MapPhoneActionToRobotAction` and
+`EEReferenceAndDelta`), the XR clutch lives entirely in the example loop's `Clutch` class. It emits
+an **absolute** EE pose, so there is no `EEReferenceAndDelta` stage and no delta accumulation in the
+processor — `MapXRControllerActionToRobotAction` is a pure, stateless per‑frame mapping.
+
+The clutch latches its engage origin on the squeeze **engage edge** (the moment the squeeze crosses
+`clutch_threshold`) and drives the EE from the motion _relative_ to that origin, so the arm does not
+teleport on engage. On **every** engage — startup and mid‑task re‑clutch alike — the home
+_position_ is latched from forward kinematics on the arm's **measured joints**, so the home equals
+where the arm physically is even if it moved while disengaged, and the engage is jump‑free. The
+home _orientation_ keeps the last commanded rotation: the 5‑DOF arm tracks orientation only
+softly, so latching the measured wrist orientation would inject its tracking offset into the
+command on every re‑clutch.
+
+## Controls
+
+- **Squeeze / grip** — the **clutch** (deadman). Hold it past `clutch_threshold` to engage
+  teleoperation; release to pause. Each engage re‑captures the origin, so you can reposition
+  your hand while paused and re‑engage without the arm jumping (index/clutch style).
+- **Trigger** — the **gripper**, controlled **analog**. The jaw tracks the trigger
+  proportionally — a half‑pressed trigger leaves the jaw half‑closed — via a closedness in
+  `[0, 1]` (0 = open, 1 = closed) that maps to an absolute gripper joint target.
+- **Controller orientation** — the **wrist**. The clutch rebases the controller orientation
+  (engage‑relative, base‑frame) into a soft IK orientation target the wrist tracks alongside
+  position. On the 5‑DOF SO‑101 the wrist follows the hand only partially by design — see
+  `orientation_weight` below.
+
+## Get started
+
+### Step 1: Create the teleoperator
+
+```python
+# Run from the repo root so the `examples` package is importable.
+from examples.isaac_teleop_to_so101.isaac_teleop import XRController, XRControllerConfig
+
+teleop_config = XRControllerConfig(
+    hand_side="right",      # "left" or "right" controller
+    clutch_threshold=0.5,   # squeeze value above which the clutch engages
+)
+teleop_device = XRController(teleop_config)
+```
+
+`XRController.get_action()` returns the **raw** base‑frame controller pose, not a clutch‑rebased
+target: `grip_pos` (3,) `[x, y, z]` [m] and `grip_quat` (4,) `[qx, qy, qz, qw]` in the robot base
+frame, plus scalar `squeeze` and `trigger` analog values in `[0, 1]`. The example loop's `Clutch`
+turns these into the absolute `ee_pose`, and the squeeze is thresholded by the loop against
+`clutch_threshold` to engage.
+
+### Step 2: Connect
+
+Calling `teleop_device.connect()` first auto-launches the CloudXR runtime (unless you opted out —
+see [Set up CloudXR and connect a headset](#set-up-cloudxr-and-connect-a-headset); this blocks for
+~30s and on the first run prompts for the EULA on stdin), then starts the Isaac Teleop
+[`TeleopSession`](https://nvidia.github.io/IsaacTeleop/main/getting_started/teleop_session.html)
+(opens the OpenXR session and discovers the controllers). XR controllers are self‑calibrating, so
+there is no manual calibration step — the clutch handles re‑centering each time you engage. Pair
+`connect()` with a `try/finally` that calls `disconnect()` so the session tears down before the
+runtime on exit/Ctrl-C.
+
+### Step 3: Run the example
+
+The example assumes you configured your robot (SO‑101 follower) and set the correct serial port.
+
+The **robot URDF and its meshes are fetched automatically** on first run: the XR device downloads
+the SO-101 URDF from the
+[`lerobot/robot-urdfs` Hugging Face bucket](https://huggingface.co/buckets/lerobot/robot-urdfs/tree/so101)
+into the LeRobot cache (`HF_LEROBOT_HOME/robot-urdfs/so101/`) and reuses it after, so there is no
+separate download step :
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower --robot.port=/dev/ttyACM0 \
+    --robot.id=so101_follower_arm --teleop.type=xr_controller
+```
+
+The CLI is `lerobot-teleoperate`-style (draccus): `--robot.*` configures the SO-101 follower and
+`--teleop.type` selects the Isaac input device (`xr_controller` | `so101_leader`), with
+`--teleop.*` its device knobs. `--teleop.type=xr_controller` runs the XR-controller path described
+above. The startup safety contract: by default it slews all joints to a default reset pose over
+`--reset_duration` seconds (`--reset_to_origin=false` keeps the arm where it is), then seeds the
+clutch home from the arm's measured pose so the first engage is jump-free; the follower is
+commanded only while the clutch is engaged.
+
+**Customizing the reset pose.** The reset pose ships as a built-in default (a comfortable mid-range
+pose) and works out of the box — you do **not** need to record anything. To tailor it to your setup,
+back-drive the arm to the pose you want and run
+`python -m examples.isaac_teleop_to_so101.override_reset_pose --id <robot.id>`; it writes the
+current joints to a per-arm file in the LeRobot cache
+(`HF_LEROBOT_HOME/reset_poses/<robot.name>/<robot.id>.json`, keyed like calibration), which then takes
+priority over the built-in default on the next run. Because it lives in the user-local cache (not
+the repo), your override stays on your machine, and both `teleoperate` and `record` honor it
+when launched with the same `--robot.id`.
+
+The other device, `--teleop.type=so101_leader`, mirrors the follower 1:1 from a back-drivable
+SO-101 _leader arm_ whose joints are streamed by Isaac Teleop's native `so101_leader` plugin (no
+clutch, no IK — the leader and follower share the SO-101 kinematics).
+
+The `so101_leader_plugin` binary is a C++ plugin that is **not** part of the `isaacteleop` pip
+package — you build it from the Isaac Teleop source tree. Follow
+[Build Isaac Teleop from source](https://nvidia.github.io/IsaacTeleop/main/getting_started/build_from_source/index.html)
+(in short, from your Isaac Teleop checkout: `cmake -B build && cmake --build build --parallel &&
+cmake --install build`); the build installs the plugins under `<IsaacTeleop>/install/plugins/`, so
+the binary lands at `install/plugins/so101_leader/so101_leader_plugin` — the `--launch_plugin` path
+below. See the plugin's own `README.md` (next to the binary) for its serial/calibration details.
+
+Point `--teleop.port` at the physical leader's serial port and `--launch_plugin` at that plugin
+binary to have the script spawn it after CloudXR is up:
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower --robot.port=/dev/ttyACM0 \
+    --robot.id=so101_follower_arm --teleop.type=so101_leader \
+    --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \
+    --launch_plugin=/code/Teleop/install/plugins/so101_leader/so101_leader_plugin
+```
+
+(Note `so101_leader` here is the _Isaac_ leader, resolved against the Isaac Teleop device
+registry, distinct from `lerobot-teleoperate`'s serial `so101_leader`.) When a `--teleop.port` is
+set, the plugin's tick→radian calibration is inferred from `--teleop.id` and passed to the plugin
+as its third positional arg — the LeRobot-format JSON at
+`HF_LEROBOT_CALIBRATION/teleoperators/so_leader/<id>.json`, the same file the serial SO-101 leader
+uses (`lerobot-calibrate --teleop.type=so101_leader --teleop.id=<id>`). If it is missing the script
+warns and the plugin uses built-in defaults. Run `python -m examples.isaac_teleop_to_so101.teleoperate --help` for all flags. Its
+startup safety contract: by default the follower is
+slewed to the leader's first reading over `--align_duration` seconds (`--align=false` to skip) so
+the arm does not snap when the mirror begins, and while the leader stream is stale the follower is
+held at its measured pose.
+
+The URDF fetch uses `huggingface_hub` (already a LeRobot dependency) against the public
+`lerobot/robot-urdfs` bucket, so it needs no login. It is cached under
+`HF_LEROBOT_HOME/robot-urdfs/so101/`; delete that folder to force a re‑download.
+
+Then, in your headset: squeeze and hold the grip to engage, move the controller to drive the
+arm, twist/tilt it to orient the wrist, and press the trigger to close the gripper
+(proportionally — release to open).
+
+To record a dataset (not just teleoperate), use `record.py` in the same folder. It dispatches on
+`--teleop.type` (`xr_controller` | `so101_leader`) exactly like `teleoperate.py`, so either device
+can drive the follower, and it saves the commanded joints to a LeRobot dataset (`lerobot-record`-style
+`--dataset.*` flags). See its module docstring for the full CLI and the keyboard recording shortcuts.
+
+## Important pipeline steps and options
+
+The clutch already produces an absolute base‑frame pose, so the processor side is a thin
+**absolute‑pose** path — there is no frame remap, no delta accumulation, and no
+`EEReferenceAndDelta` stage.
+
+- `MapXRControllerActionToRobotAction` is a stateless per‑frame mapping from the device output to
+  the IK input contract. It writes the absolute base‑frame position, encodes the absolute
+  orientation as a rotvec target, and inverts the closedness into a motor gripper target:
+
+  ```python
+  action["ee.x"], action["ee.y"], action["ee.z"] = ee_pose[:3]   # absolute, base frame [m]
+  action["ee.wx"], action["ee.wy"], action["ee.wz"] = orient_rotvec  # orientation target (rotvec)
+  action["ee.gripper_pos"] = (1 - closedness) * 100   # motor units; SO-101 calibrates 100 = open
+  ```
+
+  The gripper polarity (`100 = open, 0 = closed`) is a hardware‑calibration convention in the source — flip it there if the jaw opens when it should close.
+
+- `EEBoundsAndSafety` clamps the EE to a workspace and rate‑limits per‑frame jumps. The clutch's
+  no‑teleport keeps frames small, so `max_ee_step_m` mostly catches transient controller tracking
+  glitches. The z floor is `0.0` (the table plane) so a stray target cannot drive the EE below the
+  table; x/y stay at the loose `[-1, 1]` m box. Set `raise_on_jump=False` so an over‑limit frame is
+  **clamped and warned** instead of raising — a crash mid‑loop would leave the arm uncontrolled:
+
+  ```python
+  EEBoundsAndSafety(
+      end_effector_bounds={"min": [-1.0, -1.0, 0.0], "max": [1.0, 1.0, 1.0]},
+      max_ee_step_m=0.10,
+      raise_on_jump=False,
+  )
+  ```
+
+- `InverseKinematicsEEToJoints(initial_guess_current_joints=False, orientation_weight=0.01)` solves
+  closed‑loop Placo IK. SO‑101 is a 5‑DOF arm, so the IK is position‑dominant; the small
+  `orientation_weight` lets it softly track the orientation target carried in `ee.w*` so the wrist
+  follows the hand, while the under‑determined roll stays partial by design. There is **no**
+  `GripperVelocityToJoint`: the absolute `ee.gripper_pos` is passed straight to `gripper.pos`.
+  `initial_guess_current_joints=False` warm‑starts each solve from the **previous IK solution**
+  rather than re‑seeding from the measured joints, so the joint trajectory stays continuous
+  frame‑to‑frame. Tune `orientation_weight` on hardware — too high fights position tracking, too
+  low ignores the orientation command.
+
+The example also gates safety at the loop level: after the startup reset slew (on by default —
+pass `--reset_to_origin=false` to keep the arm where it is), it commands the robot **only while
+the clutch is engaged**, and re‑sends the measured joints while disengaged, so releasing the
+clutch freezes the arm in place.
+
+See the [Processors for Robots and Teleoperators](./processors_robots_teleop) guide for more on
+adapting the pipeline to other robots.
+
+## Troubleshooting
+
+- **`ModuleNotFoundError: isaacteleop`** — the `isaacteleop` package is not installed in the
+  active environment. Re-run the install command at the top of this guide:
+  `uv pip install "isaacteleop[cloudxr,retargeters-lite]~=1.3.131"`.
+- **No controllers found** — make sure the CloudXR runtime is running, the firewall ports are
+  whitelisted, and the headset is connected (see
+  [Set up CloudXR and connect a headset](#set-up-cloudxr-and-connect-a-headset) and the Isaac
+  Teleop [Quick Start](https://nvidia.github.io/IsaacTeleop/main/getting_started/quick_start.html)).
+- **CloudXR auto-launch failed** — `connect()` raises a `RuntimeError` if the runtime does not
+  come up within its startup timeout. Check the launcher logs under `~/.cloudxr/logs`. Common
+  causes: the EULA was never accepted (run `python -m isaacteleop.cloudxr --accept-eula` once,
+  interactively — the auto-launch prompts on stdin and hangs headless), or the runtime is already
+  running externally (set `LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1` or `auto_launch_cloudxr=False` to
+  skip the auto-launch).
+- **Arm does not move** — the clutch is a deadman: you must hold the squeeze/grip past
+  `clutch_threshold`. Lower the threshold if your controller's squeeze is reported softly.
+- **Motion feels misaligned** — confirm the headset/play space orientation. The controller stream
+  is rebased into the robot base frame by the `base_T_anchor` transform on `XRControllerConfig`
+  (default: standard OpenXR → robot axis convention); adjust it if your anchor frame differs.
+
+## Learn more
+
+NVIDIA Isaac Teleop documentation ([docs home](https://nvidia.github.io/IsaacTeleop/),
+[GitHub](https://github.com/NVIDIA/IsaacTeleop)):
+
+- [Quick Start](https://nvidia.github.io/IsaacTeleop/main/getting_started/quick_start.html) —
+  install, run the CloudXR server, connect a headset, run a teleop example.
+- [TeleopSession](https://nvidia.github.io/IsaacTeleop/main/getting_started/teleop_session.html) —
+  the session API `XRController` wraps.
+- [Retargeting interface](https://nvidia.github.io/IsaacTeleop/main/references/retargeting/index.html)
+  and [architecture overview](https://nvidia.github.io/IsaacTeleop/main/overview/architecture.html) —
+  how source nodes and retargeters compose into a pipeline.
+- [Build from source](https://nvidia.github.io/IsaacTeleop/main/getting_started/build_from_source/index.html) —
+  build `isaacteleop` (and its C++ plugins, including the `so101_leader` plugin used above) from a
+  local checkout.
+- [System Requirements](https://nvidia.github.io/IsaacTeleop/main/references/requirements.html) and
+  the [CloudXR SDK docs](https://docs.nvidia.com/cloudxr-sdk) — supported platforms, GPUs,
+  CloudXR/OpenXR runtime versions, and headsets.
@@ -0,0 +1,171 @@
+# Third-Party Robots & Teleoperators
+
+The LeRobot ecosystem extends far beyond its officially supported hardware. Thanks to LeRobot's plugin architecture, the community has built integrations for a wide range of robot arms and teleoperation devices — from industrial manipulators to affordable hobbyist platforms, VR headsets, haptic devices, and full arm-plus-leader kits. This page showcases community-maintained integrations you can use for teleoperation, data collection, and policy deployment.
+
+<Tip>
+These projects are developed and maintained by third parties. Please refer to each repository for installation instructions, hardware requirements, and support.
+</Tip>
+
+## Industrial & Collaborative Arms
+
+**[lerobot-robot-xarm](https://github.com/SpesRobotics/lerobot-robot-xarm)** — by SpesRobotics
+
+xArm integration for LeRobot, bringing UFACTORY's popular collaborative arm series into the LeRobot workflow.
+
+**[lerobot_trossen](https://github.com/TrossenRobotics/lerobot_trossen)** — by Trossen Robotics
+
+Official hardware integrations from Trossen Robotics, makers of the WidowX and ALOHA-style arms widely used in robot learning research.
+
+**[lerobot_lebai](https://github.com/lebai-robotics/lerobot_lebai)** — by Lebai Robotics
+
+Integration for Lebai collaborative robot arms, bringing them into the LeRobot teleoperation and recording workflow.
+
+**[LeFranX](https://github.com/wengmister/LeFranX)** — by wengmister
+
+LeRobot extension for the Franka robot paired with the XHand dexterous hand. An instantiation of the LeVR framework, combining a research-grade arm with VR-based teleoperation.
+
+**[UR5e-LeRobot](https://github.com/yechen056/UR5e-LeRobot)** — by yechen056
+
+LeRobot extension for the Universal Robots UR5e, with both single-arm and bimanual support.
+
+**[lerobot_ur5e_auto](https://github.com/scy-v/lerobot_ur5e_auto)** — by scy-v
+
+Automated data collection for a mobile UR5e platform, built on LeRobot — great for scaling up dataset creation with minimal human supervision.
+
+**[lerobot_ur5e_gello](https://github.com/F-Fer/lerobot_ur5e_gello)** — by F-Fer
+
+Custom LeRobot plugins for a UR5e follower and GELLO leader arm, with ready-to-use scripts for data collection and VLA policy inference.
+
+## Research & Learning Platforms
+
+**[lerobot-arx5](https://github.com/villekuosmanen/lerobot-arx5)** — by villekuosmanen
+
+An ARX5 robot arm plugin for LeRobot, integrating this compact, learning-friendly manipulator into the ecosystem.
+
+**[lerobot_robot_piper (AgRobotics Research)](https://github.com/AgRoboticsResearch/lerobot_robot_piper)** — by AgRoboticsResearch
+
+Integration of the AgileX Piper arm with LeRobot, developed in the context of agricultural robotics research.
+
+**[lerobot_robot_piper (WeGo Robotics)](https://github.com/WeGo-Robotics/lerobot_robot_piper)** — by WeGo-Robotics
+
+Multi-arm teleoperation plugin for the AgileX Piper robot, integrating with LeRobot for data collection and policy deployment.
+
+## Affordable & Hobbyist Arms
+
+**[fashionstar-lerobot-robot-cello](https://github.com/servodevelop/fashionstar-lerobot-robot-cello)** — by servodevelop
+
+LeRobot integration for the FashionStar Cello robot arm.
+
+**[fashionstar-lerobot-robot-viola](https://github.com/servodevelop/fashionstar-lerobot-robot-viola)** — by servodevelop
+
+LeRobot integration for the FashionStar Viola robot arm — a sibling to the Cello integration above.
+
+**[lerobot-robot-seeed-b601](https://github.com/Seeed-Projects/lerobot-robot-seeed-b601)** — by Seeed Studio
+
+Integration for Seeed Studio's reBot Arm B601, enabling the low-cost B601 to be used as a follower arm within LeRobot.
+
+## Multi-Device & Specialized Integrations
+
+**[lerobot-robot-ugo-pro](https://github.com/ugo-plus/lerobot-robot-ugo-pro)** — by ugo (ugo-plus)
+
+ugo Pro integration for LeRobot, bringing this service robot platform into the LeRobot ecosystem.
+
+**[lerobot_robot_lekiwi_pincopen](https://github.com/zuoxingdong/lerobot_robot_lekiwi_pincopen)** — by zuoxingdong
+
+A drop-in plugin that lets unmodified LeRobot drive a LeKiwi mobile manipulator built with STS3250 servos on the main arm joints and a PincOpen gripper, with tunable servo parameters exposed as config fields — no source edits required.
+
+**[lerobot_robot_ros2_zenoh](https://github.com/ROBOTIS-GIT/lerobot_robot_ros2_zenoh)** — by ROBOTIS
+
+A ROS 2 (Zenoh-based) robot integration for LeRobot, letting you drive ROS 2 robots through the LeRobot interface.
+
+**[lerobot-robot-dummy](https://github.com/KillingJacky/lerobot-robot-dummy)** — by KillingJacky
+
+A virtual follower arm for debugging: it drives no hardware and instead prints the actions it receives (with an optional silent mode), handy for testing leaders and pipelines. Defaults to Seeed B601 joint names, with configurable motor names for other leaders.
+
+## Teleoperators
+
+**[lerobot-teleoperator-teleop](https://github.com/SpesRobotics/lerobot-teleoperator-teleop)** — by SpesRobotics
+
+Phone and VR teleoperation integration for LeRobot, one of the community plugins referenced in the official Bring Your Own Hardware guide.
+
+**[lerobot-teleoperator-spacemouse](https://github.com/Jas000n/lerobot-teleoperator-spacemouse)** — by Jas000n
+
+Turns a 3Dconnexion SpaceMouse into a 6-DoF teleoperator, with built-in inverse kinematics for SO-101/SO-ARM followers, a direct end-effector mode, axis remapping, and custom-URDF profiles.
+
+**[vr-teleop-kit](https://github.com/Dream-Machines-Robotics/vr-teleop-kit)** — by Dream-Machines-Robotics
+
+An open-source VR teleoperation kit that drives robot arms from a Meta Quest (WebXR) headset via differential inverse kinematics, exposed as a drop-in LeRobot `Teleoperator` (single-arm and bimanual).
+
+**[lerobot-teleoperator-pico4](https://github.com/xensedyl/lerobot-teleoperator-pico4)** — by xensedyl
+
+Teleoperation plugin driving LeRobot from a PICO 4 VR headset, with a companion [hand-tracking variant](https://github.com/xensedyl/lerobot-teleoperator-pico4-hand).
+
+**[lerobot_teleoperator_yamactiveleader](https://github.com/uynitsuj/lerobot_teleoperator_yamactiveleader)** — by uynitsuj
+
+Active YAM teleop leader device integration for LeRobot.
+
+**[lerobot_teleoperator_omy](https://github.com/charlie8612/lerobot_teleoperator_omy)** — by charlie8612
+
+LeRobot teleoperator plugin for the ROBOTIS OMY-L100 6-DoF leader arm (no ROS 2 required).
+
+**[lerobot-teleoperator-deltas-gamepad](https://github.com/jpizarrom/lerobot-teleoperator-deltas-gamepad)** — by jpizarrom
+
+Teleoperate LeRobot with a standard gamepad, sending incremental (delta) end-effector commands.
+
+**[lerobot_teleoperator_inverse3](https://github.com/chohh7391/lerobot_teleoperator_inverse3)** — by chohh7391
+
+Teleoperator plugin for the Haply Inverse3 haptic device.
+
+**[lerobot_teleoperator_omega7](https://github.com/hzhz112/lerobot_teleoperator_omega7)** — by hzhz112
+
+Teleoperator plugin for the Force Dimension omega.7 haptic device.
+
+**[lerobot-teleoperator-arx5](https://pypi.org/project/lerobot-teleoperator-arx5/)** — by villekuosmanen
+
+ARX5 leader/teleoperator plugin — the leader counterpart to the ARX5 robot plugin listed above, from the same author.
+
+**[lerobot-teleoperator-seeed-b601](https://github.com/Seeed-Projects/lerobot-teleoperator-seeed-b601)** — by Seeed Studio
+
+Leader-arm teleoperator for the Seeed reBot Arm B601 (Damiao CAN motors), pairing with the B601 follower listed above.
+
+**[lerobot-teleoperator-rebot-arm-102](https://pypi.org/project/lerobot-teleoperator-rebot-arm-102/)** — by Seeed Studio
+
+reBot Arm 102 leader arm (FashionStar UART servos) designed to teleoperate the Seeed reBot B601 follower.
+
+**[lerobot-teleoperator-pipermate](https://pypi.org/project/lerobot-teleoperator-pipermate/)** — by Welt-liu
+
+PiperMate leader teleoperator (FashionStar UART servos) for the AgileX Piper arm.
+
+**[lerobot-teleoperator-livekit](https://pypi.org/project/lerobot-teleoperator-livekit/)** — by binhpham_lk
+
+Robot-side teleoperator that receives commands over a LiveKit Portal (WebRTC), enabling remote teleoperation across the network.
+
+## Robots + Teleoperators
+
+**[Nextis-AIRA-3D](https://github.com/robertorobotics/Nextis-AIRA-3D)** — by robertorobotics
+
+An open-source, 3D-printable 7-DoF arm that ships as a LeRobot plugin (not a fork), registering both a robot (`aira_follower`) and its Dynamixel leader teleoperator (`aira_leader`) for teleoperation, recording, and training.
+
+**[lerobot_yam](https://github.com/pravsels/lerobot_yam)** — by pravsels
+
+A plugin suite for the YAM arm shipping both a follower robot (`yam_follower`, CAN-driven) and a GELLO leader teleoperator (`yam_leader`), plus shared utilities — installable together or component-by-component (e.g. follower-only for policy inference).
+
+**[leros2](https://github.com/ngres/leros2)** — by ngres
+
+Maps ROS 2 topics and actions to LeRobot robots and teleoperators, bridging existing ROS 2 hardware into the LeRobot interface on both the robot and teleoperator sides.
+
+**[lerobot_robot_bi_so101_follower](https://github.com/SIGRobotics-UIUC/lerobot_robot_bi_so101_follower)** — by SIGRobotics-UIUC
+
+A bimanual (dual-arm) SO-101 setup: a follower robot package paired with its [bi-so101 leader teleoperator](https://pypi.org/project/lerobot-teleoperator-bi-so101-leader/) for dual-arm manipulation.
+
+**[trlc-dk1](https://github.com/robot-learning-co/trlc-dk1)** — by The Robot Learning Company
+
+An open-source dev kit for AI-native robotics. The repo ships a `lerobot_robot_trlc_dk1` plugin (auto-detected via LeRobot's plugin conventions despite the repo name) registering single-arm `dk1_follower`/`dk1_leader` and bimanual `bi_dk1_follower`/`bi_dk1_leader` types.
+
+**[hex_lerobot_drivers](https://github.com/hexfellow/hex_lerobot_drivers)** — by hexfellow
+
+A full suite of drop-in plugins for HEXFELLOW devices, published individually on PyPI: Hex Arm robots (`hex_arm`, `hex_arm_double`, and a `hex_arm_sim` MuJoCo variant), matching leader teleoperators (`hello`, `hex_arm`, and their double-arm versions), and cameras (see the [Cameras & Sensors](./third_party_sensors) page).
+
+## Contributing
+
+Built your own LeRobot hardware integration? The plugin system makes it straightforward to add new robots and teleoperators — check out the [Bring Your Own Hardware](./integrate_hardware) guide to get started, and share your project with the community!
@@ -0,0 +1,25 @@
+# Third-Party Cameras & Sensors
+
+Beyond the cameras natively supported by LeRobot (OpenCV, Intel RealSense, ZMQ, Reachy 2), the community has published drop-in camera plugins using the `lerobot_camera_` package convention. Because LeRobot auto-discovers any installed package prefixed with `lerobot_camera_` and registers its `CameraConfig` subclass, these work with unmodified LeRobot — just `pip install` and reference the new camera `type` from the CLI. This page collects community-maintained camera and sensor integrations.
+
+<Tip>
+These projects are developed and maintained by third parties. Please refer to each repository for installation instructions, hardware requirements, and support.
+</Tip>
+
+## Cameras & Vision Sensors
+
+**[lerobot-camera-xense](https://github.com/xensedyl/lerobot-camera-xense)** — by xensedyl
+
+A drop-in camera plugin (registers the `xense` camera type) for Xense vision-based tactile sensors via `xensesdk>=2.0.0`. Exposes rectified/difference images for the standard image observation path, plus richer outputs — depth, 2D markers, force fields, force resultants, and 3D mesh — with a per-sensor process backend for multi-sensor setups.
+
+**[hex_lerobot_drivers cameras](https://github.com/hexfellow/hex_lerobot_drivers)** — by hexfellow
+
+Part of HEXFELLOW's LeRobot plugin suite, providing two camera plugins published on PyPI: [`lerobot_camera_berxel`](https://pypi.org/project/lerobot-camera-berxel/) for Berxel (depth) cameras, and [`lerobot_camera_dummy`](https://pypi.org/project/lerobot-camera-dummy/), a simulated MuJoCo camera for the Hex Arm useful for teleoperation and recording without physical camera hardware. (The same repo also ships Hex Arm robots and leader teleoperators — see the [Robots & Teleoperators](./third_party_robots) page.)
+
+**[lerobot_camera_imageclient](https://github.com/CoNG-harvard/lerobot_camera_imageclient)** — by CoNG-harvard
+
+A LeRobot camera plugin that sources frames from an external image client, following the `lerobot_camera_` auto-discovery convention.
+
+## Contributing
+
+Built your own LeRobot camera or sensor integration? Package it as an installable `lerobot_camera_<name>` plugin and it will be auto-discovered by the LeRobot CLI — see the [Bring Your Own Hardware](./integrate_hardware) guide and the [Cameras](./cameras) reference to get started, then share your project with the community!
@@ -6,12 +6,11 @@ Encoding frames into an MP4 is a full FFmpeg pipeline: choice of encoder, pixel

 You can set these parameters from the CLI with `--dataset.rgb_encoder.<field>` (e.g. with `lerobot-record` or `lerobot-rollout`). The same block applies to every camera video stream in that run.

-<Tip>
-  Video storage must be on for `rgb_encoder` to have any effect —
-  `use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
-  recording default). With video off, inputs stay as images and `rgb_encoder` is
-  ignored.
-</Tip>
+> [!TIP]
+> Video storage must be on for `rgb_encoder` to have any effect —
+> `use_videos=True` in Python APIs, or `--dataset.video=true` on the CLI (the
+> recording default). With video off, inputs stay as images and `rgb_encoder` is
+> ignored.

 For details on **when** frames are written vs. encoded (streaming vs. post-episode), queues, and other top-level `--dataset.*` switches, see [Streaming Video Encoding](./streaming_video_encoding). For an encoding-parameter comparison and experiments, see the [video-benchmark Space](https://huggingface.co/spaces/lerobot/video-benchmark).

@@ -43,12 +42,10 @@ lerobot-record \

 ## Tuning parameters

-<Tip warning={true}>
-The defaults are tuned to balance **compression ratio**, **visual quality**, and **decoding/seek speed** for typical robotics datasets. Changing them can affect both recording (CPU load, frame drops) and training (decoding throughput, image quality).
-
-Only override these parameters if you have a specific reason to, and measure the impact on your pipeline before relying on the new settings.
-
-</Tip>
+> [!WARNING]
+> The defaults are tuned to balance **compression ratio**, **visual quality**, and **decoding/seek speed** for typical robotics datasets. Changing them can affect both recording (CPU load, frame drops) and training (decoding throughput, image quality).
+>
+> Only override these parameters if you have a specific reason to, and measure the impact on your pipeline before relying on the new settings.

 All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.

@@ -69,25 +66,92 @@ All flags below are prefixed with `--dataset.rgb_encoder.` on the CLI.

 Depth maps (Intel RealSense, Reachy 2) are stored as their **own video streams** alongside the RGB streams. Raw depth (`uint16` millimetres or `float32` metres) can't survive an 8-bit codec, so LeRobot **quantizes** each map to a 12-bit code (`[0, 4095]`) — logarithmically by default, to match the `1/depth` error profile of depth sensors — then packs it into a high-bit-depth pixel format (`gray12le`) and encodes it with a 12-bit codec.

-```mermaid
-flowchart LR
-    A["Raw depth (uint16 mm / float32 m)"] --> B["Clip to depth_min, depth_max"]
-    B --> C["Quantize to 12-bit code 0–4095 (log or linear)"]
-    C --> D["Pack into gray12le"]
-    D --> E["Encode video (hevc Main 12)"]
-    E --> F[("MP4 + metadata: depth_min/max, shift, use_log")]
-    F -. "load time (depth_output_unit)" .-> G["Dequantize to mm or m"]
-
-    classDef input fill:#e3f2fd,stroke:#1565c0,color:#0d47a1;
-    classDef encode fill:#ede7f6,stroke:#5e35b1,color:#311b92;
-    classDef store fill:#fff8e1,stroke:#f9a825,color:#e65100;
-    classDef load fill:#e8f5e9,stroke:#2e7d32,color:#1b5e20;
-
-    class A input;
-    class B,C,D,E encode;
-    class F store;
-    class G load;
-```
+<div style="margin:28px 0;padding:14px 0;">
+  <div style="margin:0 auto;display:flex;flex-wrap:wrap;justify-content:center;align-items:stretch;gap:6px;font-family:'Source Sans 3',ui-sans-serif,system-ui,sans-serif;font-size:14px;font-weight:600;color:#1B1B1D;">
+    <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#DBEAFE;color:#1D4ED8;border-radius:9px;padding:8px 12px;">
+      <span>Raw depth</span>
+      <span style="font-size:11px;font-weight:400;color:#3B6FD4;white-space:nowrap;">
+        uint16 mm
+        <br />
+        float32 m
+      </span>
+    </span>
+    <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+      →
+    </span>
+    <div style="border:2px dashed #C4B5FD;border-radius:13px;padding:18px 12px 12px;position:relative;display:flex;align-items:stretch;gap:6px;">
+      <span style="position:absolute;top:-10px;left:12px;background:#fff;padding:0 6px;font-size:11px;font-weight:700;color:#7E22CE;text-transform:uppercase;letter-spacing:0.5px;white-space:nowrap;">
+        Record time
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Clip</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          to [depth_min,
+          <br />
+          depth_max]
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Quantize</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          12-bit codes 0–4095
+          <br />
+          log (default) or linear
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Pack</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          into gray12le
+          <br />
+          plane
+        </span>
+      </span>
+      <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+        →
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#F3E8FF;color:#7E22CE;border-radius:9px;padding:8px 12px;">
+        <span>Encode</span>
+        <span style="font-size:11px;font-weight:400;color:#9061C2;white-space:nowrap;">
+          HEVC
+          <br />
+          Main 12
+        </span>
+      </span>
+    </div>
+    <span style="display:flex;align-items:center;font-size:16px;color:#C3CBD9;">
+      →
+    </span>
+    <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#FEF3C7;color:#B45309;border-radius:9px;padding:8px 12px;">
+      <span>MP4</span>
+      <span style="font-size:11px;font-weight:400;color:#C77D18;white-space:nowrap;">
+        stored
+        <br />
+        stream
+      </span>
+    </span>
+    <span style="display:flex;align-items:center;font-size:16px;color:#34A06B;">
+      →
+    </span>
+    <div style="border:2px dashed #6EE7B7;border-radius:13px;padding:18px 12px 12px;position:relative;display:flex;align-items:center;gap:6px;">
+      <span style="position:absolute;top:-10px;left:12px;background:#fff;padding:0 6px;font-size:11px;font-weight:700;color:#047857;text-transform:uppercase;letter-spacing:0.5px;white-space:nowrap;">
+        Load time
+      </span>
+      <span style="display:flex;flex-direction:column;justify-content:center;align-items:center;text-align:center;gap:2px;background:#D1FAE5;color:#047857;border-radius:9px;padding:8px 12px;">
+        <span>Dequantize</span>
+        <span style="font-size:11px;font-weight:400;color:#059669;white-space:nowrap;">
+          to mm / m
+        </span>
+      </span>
+    </div>
+  </div>
+</div>

 Configure the depth pipeline through a parallel **`depth_encoder`** block (`DepthEncoderConfig`). It shares every `RGBEncoderConfig` field (`vcodec`, `pix_fmt`, `crf`, …) and adds four quantizer knobs, set via `--dataset.depth_encoder.<field>`:

@@ -168,15 +232,16 @@ After the first episode of a video stream is encoded, the encoder configuration

 Two sources contribute to the `info` block:

- **Stream-derived** (read back from the encoded MP4 with PyAV): `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `is_depth_map`, plus `audio.*` if an audio stream is present.
- **Encoder-derived** (taken from `RGBEncoderConfig` or `DepthEncoderConfig`): `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`.
+| Source              | Where it comes from                                   | Fields                                                                                                                  |
+| ------------------- | ----------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
+| **Stream-derived**  | Read back from the encoded MP4 with PyAV.             | `video.height`, `video.width`, `video.codec`, `video.pix_fmt`, `video.fps`, `video.channels`, `is_depth_map`, `audio.*` |
+| **Encoder-derived** | Taken from `RGBEncoderConfig` / `DepthEncoderConfig`. | `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.video_backend`, `video.extra_options`               |

-<Tip>
-  This block is populated **once**, from the **first** episode. It assumes every
-  episode in the dataset was encoded with the same `rgb_encoder`. Changing
-  encoder settings partway through a recording is not supported — the
-  `info.json` will only reflect the parameters used for the first episode.
-</Tip>
+> [!IMPORTANT]
+> This block is populated **once**, from the **first** episode. It assumes every
+> episode in the dataset was encoded with the same `rgb_encoder`. Changing
+> encoder settings partway through a recording is not supported — the
+> `info.json` will only reflect the parameters used for the first episode.

 ---

@@ -184,5 +249,7 @@ Two sources contribute to the `info` block:

 When aggregating datasets with `merge_datasets`, video files are concatenated as-is (no re-encoding), and encoder fields in `info.json` are merged per-key:

- **Stream-derived fields must match** across sources: `video.codec`, `video.pix_fmt`, `video.height`, `video.width`, `video.fps`. Otherwise FFmpeg's concat demuxer fails.
- **Encoder-tuning fields are merged loosely**: `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.extra_options`. If every source agrees, the value is kept; if not, it's set to `null` (or `{}` for `video.extra_options`) and a warning is logged.
+| Merge rule         | Fields                                                                             | Behaviour                                                                                                                                                 |
+| ------------------ | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| **Must match**     | `video.codec`, `video.pix_fmt`, `video.height`, `video.width`, `video.fps`         | Stream-derived fields must match across sources, otherwise FFmpeg's concat demuxer fails.                                                                 |
+| **Merged loosely** | `video.g`, `video.crf`, `video.preset`, `video.fast_decode`, `video.extra_options` | Encoder-tuning fields. If every source agrees, the value is kept; if not, it's set to `null` (or `{}` for `video.extra_options`) and a warning is logged. |
@@ -0,0 +1,131 @@
+# Isaac Teleop → SO-101
+
+Teleoperate an SO-101/SO-100 follower arm — and record LeRobot datasets — with NVIDIA
+[Isaac Teleop](https://github.com/NVIDIA/IsaacTeleop). Two input devices ship today:
+
+- **XR (VR) controller** (`--teleop.type=xr_controller`) — the controller's grip pose drives the
+  end-effector through a squeeze-to-engage clutch and LeRobot's Cartesian IK pipeline; the analog
+  trigger drives the gripper.
+- **SO-101 leader arm** (`--teleop.type=so101_leader`) — a back-drivable leader arm mirrored 1:1
+  onto the follower via Isaac Teleop's native `so101_leader` plugin (no clutch, no IK).
+
+The full narrative guide (how the clutch works, CloudXR setup, headset pairing, tuning, and
+troubleshooting) is in the [LeRobot docs](https://huggingface.co/docs/lerobot/isaac_teleop)
+(source: `docs/source/isaac_teleop.mdx`). This README is the canonical install and usage
+reference.
+
+## Requirements
+
+- Linux workstation (see NVIDIA's
+  [system requirements](https://nvidia.github.io/IsaacTeleop/main/references/requirements.html)
+  for supported OS/GPU/headset combinations; `isaacteleop` publishes Linux wheels only).
+- An SO-101 (or SO-100) follower arm, calibrated with `lerobot-calibrate`.
+- For the XR device: a CloudXR-capable headset (e.g. Quest 3, Pico 4, Apple Vision Pro) on the
+  same network.
+- For the leader device: a second, back-drivable SO-101 leader arm and the `so101_leader` plugin
+  binary built from the Isaac Teleop source tree (see
+  [Build from source](https://nvidia.github.io/IsaacTeleop/main/getting_started/build_from_source/index.html)).
+
+## Installation
+
+This example lives in the LeRobot repository and is not part of the `lerobot` pip package, so
+work from a source checkout. From the repo root:
+
+```bash
+# LeRobot with the extras this example uses:
+#   feetech    - SO-101 serial motor bus
+#   kinematics - Placo IK solver (XR controller path)
+#   dataset    - dataset recording (record.py)
+# huggingface_hub >= 1.5 is needed by the automatic URDF fetch (Buckets API).
+uv pip install -e ".[feetech,kinematics,dataset]" "huggingface_hub>=1.5"
+
+# Isaac Teleop from public PyPI. `cloudxr` brings the CloudXR runtime bindings;
+# `retargeters-lite` is the scipy-based retargeter path that resolves on both
+# x86_64 and ARM (the full `retargeters` extra does not resolve on aarch64).
+uv pip install "isaacteleop[cloudxr,retargeters-lite]~=1.3.131" "scipy>=1.14"
+
+# Optional, x86_64 only: the full retargeter stack.
+uv pip install "isaacteleop[retargeters]~=1.3.131"
+```
+
+One-time CloudXR EULA (the auto-launch prompts on stdin and would hang on a headless machine):
+
+```bash
+python -m isaacteleop.cloudxr --accept-eula
+```
+
+## Usage
+
+Run everything from the repo root with `python -m` so the `examples` package resolves.
+
+### Teleoperate — XR controller
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate \
+    --robot.type=so101_follower \
+    --robot.port=/dev/ttyACM0 \
+    --robot.id=so101_follower_arm \
+    --teleop.type=xr_controller
+```
+
+On startup the script launches the CloudXR runtime (~30 s), prints the workstation IP to enter in
+the headset's CloudXR web client, waits for the controllers to stream, slews the arm to a reset
+pose (`--reset_to_origin=false` to skip), and then: **hold the squeeze/grip** to engage, move the
+controller to drive the arm, pull the trigger to close the gripper. Releasing the squeeze freezes
+the arm. The SO-101 URDF is fetched automatically from the `lerobot/robot-urdfs` Hugging Face
+bucket into the LeRobot cache on first run.
+
+To customize the reset pose: back-drive the arm to the pose you want, then
+
+```bash
+python -m examples.isaac_teleop_to_so101.override_reset_pose --port /dev/ttyACM0 --id so101_follower_arm
+```
+
+which writes it to `HF_LEROBOT_HOME/reset_poses/<robot.name>/<robot.id>.json`; runs with the same
+`--robot.id` use it automatically.
+
+### Teleoperate — SO-101 leader arm
+
+```bash
+python -m examples.isaac_teleop_to_so101.teleoperate \
+    --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm \
+    --teleop.type=so101_leader --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \
+    --launch_plugin=/path/to/IsaacTeleop/install/plugins/so101_leader/so101_leader_plugin
+```
+
+The follower is first slewed to the leader's pose over `--align_duration` seconds
+(`--align=false` to skip), then mirrors it 1:1. The plugin reuses the serial leader's calibration
+(`HF_LEROBOT_CALIBRATION/teleoperators/so_leader/<teleop.id>.json`).
+
+### Record a dataset
+
+`record.py` takes the same `--robot.*`/`--teleop.*`/loop flags plus `lerobot-record`-style
+`--dataset.*` flags:
+
+```bash
+python -m examples.isaac_teleop_to_so101.record \
+    --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm \
+    --teleop.type=xr_controller \
+    --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
+    --dataset.repo_id=<hf_user>/<dataset_name> \
+    --dataset.single_task="Pick up the cube" \
+    --dataset.num_episodes=3 --dataset.episode_time_s=20 --dataset.reset_time_s=5
+```
+
+Keyboard shortcuts (terminal-first, so they work over SSH): **Right/n** end episode early,
+**Left/r** re-record, **Esc/q** stop after the current episode.
+
+Run either script with `--help` for all flags.
+
+## Layout
+
+```
+isaac_teleop/            device library: session lifecycle (base.py), XRController,
+                         SO101LeaderArm, Clutch, configs, and the XR→IK processor step
+common.py                shared loop infra: device bundles, clutch/IK pipeline wiring,
+                         reset/align slews, URDF fetch, keyboard listener
+teleoperate.py           teleoperation CLI (device selected via --teleop.type)
+record.py                dataset-recording CLI (same device selection + --dataset.*)
+override_reset_pose.py   save the current joints as the per-arm reset pose
+default.env              CloudXR device-profile overrides passed to the launcher
+```
@@ -0,0 +1,17 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Isaac Teleop -> SO-101 example package."""
@@ -0,0 +1,650 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Shared device + control-loop infrastructure for the Isaac Teleop -> SO-101 examples.
+
+Consumed by ``teleoperate.py`` and ``record.py``, which both build a per-device
+:class:`Device` bundle and run the same loop: read -> (maybe command) -> hold-when-idle ->
+sleep. A :class:`Device` bundles three closures: ``compute(obs) -> RobotAction | None``
+(``None`` = hold at the measured pose while idle), ``startup``, and ``cleanup``. The devices:
+
+* ``xr_controller`` — a thin :class:`XRController` whose raw grip pose an in-loop
+  :class:`Clutch` turns into an EE target for LeRobot's Cartesian IK pipeline.
+* ``so101_leader`` — a back-drivable leader arm mirrored 1:1 into the follower.
+
+Requires the ``isaacteleop`` package and an OpenXR runtime (install instructions in this
+folder's ``README.md``). User-facing guide: ``docs/source/isaac_teleop.mdx``.
+"""
+
+import json
+import logging
+import socket
+import subprocess
+import sys
+import time
+from collections.abc import Callable
+from contextlib import suppress
+from dataclasses import dataclass
+from importlib.resources import files
+from pathlib import Path
+from typing import Protocol
+
+import numpy as np
+
+from lerobot.model.kinematics import RobotKinematics
+from lerobot.processor import (
+    RobotProcessorPipeline,
+    robot_action_observation_to_transition,
+    transition_to_robot_action,
+)
+from lerobot.robots import RobotConfig, make_robot_from_config
+from lerobot.robots.so_follower import SOFollowerConfig  # noqa: F401  (registers so101_follower)
+from lerobot.robots.so_follower.robot_kinematic_processor import (
+    EEBoundsAndSafety,
+    InverseKinematicsEEToJoints,
+)
+from lerobot.types import RobotAction, RobotObservation
+from lerobot.utils.constants import HF_LEROBOT_CALIBRATION, HF_LEROBOT_HOME, TELEOPERATORS
+from lerobot.utils.robot_utils import precise_sleep
+
+from .isaac_teleop import (
+    Clutch,
+    IsaacTeleopConfig,
+    MapXRControllerActionToRobotAction,
+    SO101LeaderArm,
+    SO101LeaderArmConfig,
+    XRController,
+)
+
+# Fixed rate [Hz] for the teleoperate loop and the pre-loop slews / connect-wait poll sleeps.
+FPS = 30
+
+# CloudXR device-profile env file passed to the launcher (see default.env in this package).
+CLOUDXR_ENV_FILE = str(files(__package__) / "default.env")
+
+
+class LoopConfig(Protocol):
+    """Structural type for the loop/launch knobs ``build_device`` and the ``setup_*`` read.
+
+    Both ``TeleoperateConfig`` and ``RecordConfig`` satisfy it, keeping ``common`` decoupled
+    from either entry point's concrete config.
+    """
+
+    teleop: IsaacTeleopConfig
+    robot: RobotConfig
+    launch_plugin: str | None
+    reset_to_origin: bool
+    reset_duration: float
+    align: bool
+    align_duration: float
+
+
+# Per-device bundle consumed by the shared loop. ``compute`` returns None to mean
+# "idle -> hold at the measured pose"; ``startup`` warms up; ``cleanup`` reaps/disconnects.
+@dataclass(frozen=True)
+class Device:
+    compute: Callable[[RobotObservation | None], RobotAction | None]
+    startup: Callable[[], None]
+    cleanup: Callable[[], None]
+
+
+def hold_action(obs: RobotObservation, motor_names: list[str]) -> dict[str, float]:
+    """Re-send the measured joints — the explicit hold when a device is idle."""
+    return {f"{name}.pos": float(obs[f"{name}.pos"]) for name in motor_names}
+
+
+class HoldLatch:
+    """Resolve the per-frame action, holding one LATCHED pose while the device is idle.
+
+    Re-sending the freshly measured joints on every idle frame would ratchet the arm
+    downward: under gravity the P-only servo settles below its goal by a steady-state
+    error, so each re-command of the measurement lowers the goal by that error again.
+    Latching the target once on the active->idle transition holds a fixed pose instead.
+    """
+
+    def __init__(self, motor_names: list[str]):
+        self._motor_names = motor_names
+        self._held: dict[str, float] | None = None
+
+    def resolve(self, action: RobotAction | None, obs: RobotObservation) -> RobotAction:
+        """Pass through an active action (clearing the latch); latch + hold when idle."""
+        if action is not None:
+            self._held = None
+            return action
+        if self._held is None:
+            self._held = hold_action(obs, self._motor_names)
+        return self._held
+
+
+def slew(
+    robot,
+    motor_names: list[str],
+    target_fn: Callable[[], dict[str, float]],
+    duration_s: float,
+) -> None:
+    """Linearly slew all joints from their current measured pose toward a target.
+
+    ``target_fn`` is called EACH step, so the leader can pass a live re-read (landing on its
+    current pose at ``alpha == 1`` for a continuous handoff) while XR passes a constant.
+    """
+    obs = robot.get_observation()
+    start = {name: float(obs[f"{name}.pos"]) for name in motor_names}
+    n_steps = max(1, int(duration_s * FPS))
+    for step in range(1, n_steps + 1):
+        alpha = step / n_steps
+        target = target_fn()
+        action = {f"{name}.pos": start[name] + alpha * (target[name] - start[name]) for name in motor_names}
+        robot.send_action(action)
+        precise_sleep(1.0 / FPS)
+
+
+# ============================================================================
+# XR controller device
+# ============================================================================
+
+# Per-frame EE rate limit [m]. With raise_on_jump=False, EEBoundsAndSafety clamps an
+# over-limit step instead of raising, absorbing a tracking glitch as one slow frame. At
+# FPS=30, 0.1 m/frame caps EE speed at ~3 m/s. (end_effector_bounds clips the absolute target.)
+MAX_EE_STEP_M = 0.1
+
+# Soft-orientation IK weight: small but nonzero so the wrist follows the hand while position
+# dominates (the 5-DOF SO-101 cannot realize an arbitrary orientation). 0.0 = position-only.
+IK_ORIENTATION_WEIGHT = 0.01
+
+
+def _ensure_so101_urdf() -> str:
+    """Return the cached SO-101 URDF path, fetching the ``so101`` folder (URDF + meshes) from
+    the public ``lerobot/robot-urdfs`` HF bucket into the LeRobot cache on first use."""
+    dest_dir = HF_LEROBOT_HOME / "robot-urdfs" / "so101"
+    urdf_path = dest_dir / "so101_new_calib.urdf"
+    # Completeness marker written only after a FULL sync: the URDF file alone is not a
+    # completeness signal (an interrupted first sync can leave the meshes it references
+    # missing, which the URDF's mere existence would then hide forever). Re-syncing is
+    # idempotent and repairs a partial cache; delete the folder to force a re-download.
+    marker = dest_dir / ".sync_complete"
+    if not marker.exists():
+        from huggingface_hub import sync_bucket
+
+        sync_bucket("hf://buckets/lerobot/robot-urdfs/so101", str(dest_dir), quiet=True)
+        marker.touch()
+    return str(urdf_path)
+
+
+# Default duration [s] for the startup reset-to-origin slew.
+RESET_DURATION_S = 5.0
+
+# Optional cached file written by override_reset_pose.py. When present it takes priority over RESET_ORIGIN_DEG.
+RESET_POSE_FILE = str(HF_LEROBOT_HOME / "reset_poses" / "{robot_name}" / "{robot_id}.json")
+
+# Reset target in each motor's native units (arm joints in degrees, gripper RANGE_0_100,
+# 100 = open). An empirically comfortable pose (elbow/wrist bent) avoiding the singularity of
+# a fully-extended arm; assumes standard calibration. Override per-arm via override_reset_pose.py.
+RESET_ORIGIN_DEG: dict[str, float] = {
+    "shoulder_pan": -4.0,
+    "shoulder_lift": -103.0,
+    "elbow_flex": 97.0,
+    "wrist_flex": 78.0,
+    "wrist_roll": -65.0,
+    "gripper": 0.0,
+}
+
+
+def _load_reset_target(reset_pose_file: Path, motor_names: list[str]) -> dict[str, float]:
+    """Return reset targets: the saved reset pose if present, else RESET_ORIGIN_DEG."""
+    if reset_pose_file.exists():
+        saved = json.loads(reset_pose_file.read_text())
+        # Fill any missing motors from the fallback dict.
+        return {name: float(saved.get(name, RESET_ORIGIN_DEG.get(name, 0.0))) for name in motor_names}
+    return {name: RESET_ORIGIN_DEG.get(name, 0.0) for name in motor_names}
+
+
+# CloudXR web client URL opened in the headset (Isaac Teleop quick start, step 5).
+_CLOUDXR_WEB_CLIENT_URL = "https://nvidia.github.io/IsaacTeleop/client"
+# WSS-proxy / self-signed-cert port the operator accepts in-browser before connecting.
+_CLOUDXR_WSS_PORT = 48322
+# How often to re-print the connection hint while waiting for the headset [s].
+_XR_CONNECT_REMINDER_S = 15.0
+# Virtual / bridge / USB-gadget interfaces a headset can't reach over the network — skip
+# by name prefix (``docker0``, compose ``br-*``, ``veth*``, libvirt ``virbr*``, and the
+# Tegra USB device-mode bridge ``l4tbr0``).
+_SKIP_IFACE_PREFIXES = ("docker", "br-", "veth", "virbr", "l4tbr")
+
+
+def _primary_ipv4() -> str | None:
+    """The workstation's primary outbound IPv4, via the UDP-socket trick (``connect()`` on a
+    datagram socket selects the egress interface without sending packets)."""
+    with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:
+        try:
+            s.connect(("8.8.8.8", 80))
+            return s.getsockname()[0]
+        except OSError:
+            return None
+
+
+def _candidate_ipv4s() -> list[tuple[str, str]]:
+    """Return ``[(interface, ipv4), ...]`` the headset might reach this workstation at.
+
+    Lists each interface's IPv4 via ``psutil`` (dropping loopback, link-local, and the
+    virtual/bridge interfaces in ``_SKIP_IFACE_PREFIXES``), primary outbound first. Falls
+    back to just the primary IP when ``psutil`` is unavailable.
+    """
+    primary = _primary_ipv4()
+    found: list[tuple[str, str]] = []
+    try:
+        import psutil
+
+        for iface, addrs in psutil.net_if_addrs().items():
+            if iface.startswith(_SKIP_IFACE_PREFIXES):
+                continue
+            for addr in addrs:
+                if addr.family != socket.AF_INET:
+                    continue
+                ip = addr.address
+                if ip.startswith("127.") or ip.startswith("169.254."):
+                    continue
+                found.append((iface, ip))
+    except Exception:
+        if primary:
+            found.append(("default", primary))
+    found.sort(key=lambda t: t[1] != primary)  # primary outbound interface first
+    return found
+
+
+def _print_xr_connect_help() -> None:
+    """Print how to connect the headset to this workstation over CloudXR."""
+    ips = _candidate_ipv4s()
+    print("\n" + "=" * 76)
+    print("Connect your XR headset to this workstation over NVIDIA CloudXR:")
+    print(f"  1. In the headset, open the CloudXR web client:  {_CLOUDXR_WEB_CLIENT_URL}")
+    print("  2. Enter this workstation's IP address:")
+    if ips:
+        for iface, ip in ips:
+            print(f"        {ip:<15}  ({iface})")
+        if len(ips) > 1:
+            print("     (use the address on the same network as your headset)")
+    else:
+        print("        <could not determine — check `hostname -I` / `ip addr`>")
+    print(f"  3. Accept the self-signed cert at https://<that-ip>:{_CLOUDXR_WSS_PORT}/ , then Connect.")
+    print("=" * 76 + "\n")
+
+
+def _wait_for_xr_controller(teleop_device: XRController) -> None:
+    """Block until the XR controller is tracked, polling ``get_action()`` and re-printing a
+    reminder every ``_XR_CONNECT_REMINDER_S``. User-paced; ``Ctrl-C`` aborts (no hard timeout).
+    """
+    _print_xr_connect_help()
+    print("Waiting for the headset controllers to start streaming…  (Ctrl-C to abort)")
+    last_reminder = time.time()
+    while True:
+        teleop_device.get_action()  # steps the session; updates is_tracking
+        if teleop_device.is_tracking:
+            print("Headset connected — controllers are streaming.")
+            return
+        if time.time() - last_reminder >= _XR_CONNECT_REMINDER_S:
+            print("…still waiting for the headset to connect (Ctrl-C to abort).")
+            last_reminder = time.time()
+        time.sleep(1.0 / FPS)
+
+
+def setup_xr(cfg: LoopConfig, robot, motor_names: list[str]) -> Device:
+    """Build the XR controller device bundle (clutch + soft-orientation IK pipeline)."""
+    kinematics_solver = RobotKinematics(
+        urdf_path=_ensure_so101_urdf(),
+        target_frame_name="gripper_frame_link",
+        joint_names=motor_names,
+    )
+
+    teleop_config = cfg.teleop  # XRControllerConfig (selected via --teleop.type=xr_controller)
+    teleop_device = XRController(teleop_config)
+
+    # The clutch (below) turns the raw grip pose into an absolute base-frame ee_pose; this
+    # pipeline maps it to joint targets: rename -> bounds/rate-limit -> IK.
+    xr_to_robot_joints_processor = RobotProcessorPipeline[tuple[RobotAction, RobotObservation], RobotAction](
+        steps=[
+            MapXRControllerActionToRobotAction(),
+            # raise_on_jump=False: an over-limit step (e.g. a tracking glitch) is clamped +
+            # warned instead of raised, since a crash mid-loop would leave the arm uncontrolled.
+            # z floor 0.0 keeps a stray target above the table; x/y stay at a loose [-1,1]m box.
+            EEBoundsAndSafety(
+                end_effector_bounds={"min": [-1.0, -1.0, 0.0], "max": [1.0, 1.0, 1.0]},
+                max_ee_step_m=MAX_EE_STEP_M,
+                raise_on_jump=False,
+            ),
+            # initial_guess_current_joints=False: warm-start from the previous IK solution so
+            # the joint trajectory stays continuous frame-to-frame.
+            InverseKinematicsEEToJoints(
+                kinematics=kinematics_solver,
+                motor_names=motor_names,
+                initial_guess_current_joints=False,
+                orientation_weight=IK_ORIENTATION_WEIGHT,
+            ),
+        ],
+        to_transition=robot_action_observation_to_transition,
+        to_output=transition_to_robot_action,
+    )
+
+    # The clutch is built in startup() (after the optional reset slew, seeded from the
+    # post-slew MEASURED pose) and shared with compute() via nonlocal.
+    clutch: Clutch | None = None
+    prev_enabled = False
+
+    def startup() -> None:
+        nonlocal clutch
+        # Connect and wait for the operator to don the headset BEFORE moving the arm, so the
+        # reset slew happens while they are watching in VR.
+        teleop_device.connect()
+        if not teleop_device.is_connected:
+            raise ValueError("Teleop is not connected!")
+        _wait_for_xr_controller(teleop_device)
+
+        if cfg.reset_to_origin:
+            reset_pose_file = Path(RESET_POSE_FILE.format(robot_name=robot.name, robot_id=robot.id))
+            target = _load_reset_target(reset_pose_file, motor_names)
+            source = str(reset_pose_file) if reset_pose_file.exists() else "hardcoded defaults"
+            print(f"Reset target source: {source}")
+            print(f"Resetting to origin over {cfg.reset_duration:.1f} s…")
+            slew(robot, motor_names, lambda: target, cfg.reset_duration)
+            print("Reset complete.")
+
+        # Seed the clutch home from the arm's measured pose (FK of the current joints) so the
+        # first engage is jump-free, whether or not a reset slew ran.
+        obs0 = robot.get_observation()
+        q_measured_deg = np.array([float(obs0[f"{name}.pos"]) for name in motor_names], dtype=float)
+        home_base_T_ee = kinematics_solver.forward_kinematics(q_measured_deg)  # noqa: N806
+        clutch = Clutch(home_base_T_ee)
+
+        print("Starting teleop loop. Squeeze and move the controller to teleoperate the robot...")
+
+    def compute(robot_obs: RobotObservation | None) -> RobotAction | None:
+        nonlocal prev_enabled
+        if clutch is None:  # set in startup(), which runs before compute()
+            raise RuntimeError("compute() called before startup(); the clutch is not initialized")
+        xr_action = teleop_device.get_action()
+        grip_pos = np.asarray(xr_action["grip_pos"], dtype=float)
+        grip_quat = np.asarray(xr_action["grip_quat"], dtype=float)
+        squeeze = float(xr_action["squeeze"])
+        trigger = float(xr_action["trigger"])
+        enabled = squeeze > teleop_config.clutch_threshold
+
+        # On the engage edge, latch the clutch home at the arm's MEASURED EE pose (FK of
+        # the live joints) and the controller origin so the per-frame delta starts at zero.
+        # Latching the last commanded pose instead would snap the arm back to it at full
+        # servo speed if the arm moved while disengaged (gravity sag, external contact).
+        is_engage_frame = enabled and not prev_enabled
+        if is_engage_frame:
+            q_measured = np.array([float(robot_obs[f"{name}.pos"]) for name in motor_names], dtype=float)
+            measured_base_T_ee = kinematics_solver.forward_kinematics(q_measured)  # noqa: N806
+            clutch.engage(grip_pos, grip_quat, measured_base_T_ee=measured_base_T_ee)
+            # Re-anchor the pipeline state at the measured pose as well: EEBoundsAndSafety's
+            # rate limiter and the IK warm start otherwise still reference the stale
+            # pre-disengage command and would fight the fresh home for several frames.
+            xr_to_robot_joints_processor.reset()
+        prev_enabled = enabled
+
+        # SAFETY GATE: command the robot ONLY while the clutch is engaged; otherwise return
+        # None so the loop holds the measured joints (releasing the clutch freezes the arm).
+        if not enabled:
+            return None
+
+        # Rebase the raw grip pose onto the EE, then run the pipeline. closedness = trigger.
+        ee_pos, ee_quat = clutch.rebase(grip_pos, grip_quat)
+        ee_action = {
+            "ee_pose": np.concatenate([ee_pos, ee_quat]).astype(np.float32),
+            "closedness": trigger,
+        }
+        return xr_to_robot_joints_processor((ee_action, robot_obs))
+
+    return Device(compute=compute, startup=startup, cleanup=teleop_device.disconnect)
+
+
+# ============================================================================
+# SO-101 leader arm device
+# ============================================================================
+
+# Default duration [s] for the startup alignment slew (follower current -> leader first pose).
+ALIGN_DURATION_S = 3.0
+
+# How long to wait for the leader plugin to start streaming before aligning / looping.
+LEADER_WARMUP_TIMEOUT_S = 20.0
+
+# The plugin converts the leader's servo ticks to radians, so it reuses the serial SO-101
+# leader's calibration, stored by lerobot-calibrate under SO101Leader.name == "so_leader".
+SO_LEADER_CALIBRATION_NAME = "so_leader"
+
+
+def _leader_calibration_path(cfg: LoopConfig) -> Path | None:
+    """Infer the calibration JSON the launched plugin should read, or None.
+
+    Path convention: ``HF_LEROBOT_CALIBRATION / teleoperators / so_leader / {--teleop.id}.json``
+    (or ``--teleop.calibration_dir`` if set). Returns None (plugin falls back to defaults) when
+    it does not exist, warning if an id was given, or when no ``--teleop.id`` is set.
+    """
+    if not cfg.teleop.id:
+        return None
+    calib_dir = cfg.teleop.calibration_dir or (
+        HF_LEROBOT_CALIBRATION / TELEOPERATORS / SO_LEADER_CALIBRATION_NAME
+    )
+    calib_path = Path(calib_dir) / f"{cfg.teleop.id}.json"
+    if calib_path.is_file():
+        return calib_path
+    print(
+        f"WARNING: no leader calibration at {calib_path}; the plugin will use built-in defaults. "
+        f"Calibrate with the serial leader (`lerobot-calibrate --teleop.type=so101_leader "
+        f"--teleop.id={cfg.teleop.id}`) or the plugin's `calibrate` subcommand."
+    )
+    return None
+
+
+def _wait_for_leader(teleop: SO101LeaderArm, timeout_s: float) -> dict[str, float]:
+    """Poll the leader until it streams a live frame; return that frame's ``{joint}.pos``.
+
+    Raises ``SystemExit`` if no live frame arrives within ``timeout_s`` (plugin not pushing,
+    wrong ``--teleop.collection_id``, or CloudXR not up).
+    """
+    print(f"Waiting up to {timeout_s:.0f}s for the so101_leader plugin to stream…")
+    deadline = time.time() + timeout_s
+    while time.time() < deadline:
+        action = teleop.get_action()
+        if teleop.is_tracking:
+            print("Leader is streaming.")
+            return action
+        time.sleep(1.0 / FPS)
+    raise SystemExit(
+        f"FAILED: leader did not stream within {timeout_s:.0f}s. Is the so101_leader plugin "
+        "running and pushing (check --teleop.collection_id)? Is CloudXR up?"
+    )
+
+
+def _maybe_launch_plugin(cfg: LoopConfig) -> subprocess.Popen | None:
+    """Spawn the so101_leader plugin if ``--launch_plugin <path>`` was given (after connect())."""
+    if cfg.launch_plugin is None:
+        return None
+    if not Path(cfg.launch_plugin).exists():
+        raise SystemExit(
+            f"plugin binary not found: {cfg.launch_plugin} (build it in the IsaacTeleop repo first)"
+        )
+    leader_port = cfg.teleop.port  # SO101LeaderArmConfig.port, forwarded to the plugin
+    backend = f"leader on {leader_port}" if leader_port else "synthetic trajectory"
+    print(f"launching plugin: {cfg.launch_plugin} ({backend})")
+    # Positional args: [device_path] [collection_id] [calibration_file]. Empty device_path ->
+    # synthetic backend. Calibration (only real hardware needs it) is appended when a port is set.
+    argv = [cfg.launch_plugin, leader_port, cfg.teleop.collection_id]
+    if leader_port:
+        calib_path = _leader_calibration_path(cfg)
+        if calib_path is not None:
+            argv.append(str(calib_path))
+            print(f"  leader calibration: {calib_path}")
+    # Spawned after connect() so it inherits the CloudXR runtime env (XR_RUNTIME_JSON, ...).
+    proc = subprocess.Popen(argv)
+    time.sleep(1.5)  # let it create its OpenXR session and start pushing
+    return proc
+
+
+def setup_leader(cfg: LoopConfig, robot, motor_names: list[str]) -> Device:
+    """Build the SO-101 leader arm device bundle (1:1 joint mirror)."""
+    teleop_config = cfg.teleop  # SO101LeaderArmConfig (selected via --teleop.type=so101_leader)
+    teleop = SO101LeaderArm(teleop_config)
+
+    plugin_proc: subprocess.Popen | None = None
+
+    def startup() -> None:
+        nonlocal plugin_proc
+        # connect() auto-launches CloudXR (unless opted out); spawn the plugin AFTER so it
+        # inherits the runtime env. The plugin is reaped in cleanup().
+        teleop.connect()
+        plugin_proc = _maybe_launch_plugin(cfg)
+
+        if not teleop.is_connected:
+            raise ValueError("Teleop is not connected!")
+
+        # Block until the leader streams a live frame (clear error if it never does).
+        _wait_for_leader(teleop, LEADER_WARMUP_TIMEOUT_S)
+
+        if cfg.align:
+            print(f"Aligning follower to leader over {cfg.align_duration:.1f}s…")
+
+            # Re-read the live leader pose once per step so alpha=1 lands on its current pose
+            # from a single coherent frame.
+            def _leader_target() -> dict[str, float]:
+                leader_now = teleop.get_action()
+                return {name: float(leader_now[f"{name}.pos"]) for name in motor_names}
+
+            slew(robot, motor_names, _leader_target, cfg.align_duration)
+            print("Alignment complete.")
+
+        print(
+            "Starting joint-mirror loop. Back-drive the leader to teleoperate the follower… (Ctrl-C to stop)"
+        )
+
+    def compute(robot_obs: RobotObservation | None) -> RobotAction | None:
+        leader_action = teleop.get_action()
+        # Hold the follower at its measured pose when the leader drops out (stale stream)
+        # rather than commanding a possibly-old target.
+        if not teleop.is_tracking:
+            return None
+        return leader_action
+
+    def cleanup() -> None:
+        # A plugin-reaping failure must not skip the session disconnect (and vice versa
+        # the disconnect runs after the plugin stops pushing on it).
+        try:
+            if plugin_proc is not None:
+                plugin_proc.terminate()
+                try:
+                    plugin_proc.wait(timeout=5)
+                except subprocess.TimeoutExpired:
+                    plugin_proc.kill()
+        finally:
+            teleop.disconnect()
+
+    return Device(compute=compute, startup=startup, cleanup=cleanup)
+
+
+# ============================================================================
+# Shared setup
+# ============================================================================
+
+
+def build_device(cfg: LoopConfig) -> tuple:
+    """Connect the follower, build the selected Isaac device, and run its pre-loop startup.
+
+    Connects the follower FIRST (so the startup slew / clutch-home seed can read live joints),
+    dispatches on ``--teleop.type``, then runs ``device.startup()`` before returning. On any
+    failure after ``connect()`` the follower is disconnected so the connection never leaks.
+
+    Returns ``(robot, device, motor_names)``.
+    """
+    # Default the CloudXR input profile to this example's default.env unless the user overrode
+    # it via --teleop.cloudxr_env_file.
+    if cfg.teleop.cloudxr_env_file is None:
+        cfg.teleop.cloudxr_env_file = CLOUDXR_ENV_FILE
+
+    # SO-101/SO-100 only (both share the SO-101 URDF), reject other followers.
+    supported_robots = {"so101_follower", "so100_follower"}
+    if cfg.robot.type not in supported_robots:
+        raise ValueError(
+            f"This example only supports SO-101/SO-100 followers ({sorted(supported_robots)}), "
+            f"but got --robot.type={cfg.robot.type}."
+        )
+
+    # The degree-based pipeline relies on --robot.use_degrees (default True).
+    robot = make_robot_from_config(cfg.robot)
+    # Connect FIRST so the startup slew and clutch-home seed can read live joints.
+    robot.connect()
+    # Everything after connect() can fail; this runs outside the callers' try/finally, so
+    # disconnect the follower on any failure to avoid leaking the connection.
+    device: Device | None = None
+    try:
+        # Joint names in action order, read from {name}.pos action features (robot-agnostic).
+        motor_names = [key.removesuffix(".pos") for key in robot.action_features if key.endswith(".pos")]
+
+        if isinstance(cfg.teleop, SO101LeaderArmConfig):
+            device = setup_leader(cfg, robot, motor_names)
+        else:
+            device = setup_xr(cfg, robot, motor_names)
+
+        device.startup()
+    except BaseException:
+        # Reap a partially-started device, then always disconnect the follower.
+        if device is not None:
+            with suppress(Exception):
+                device.cleanup()
+        robot.disconnect()
+        raise
+
+    return robot, device, motor_names
+
+
+# ============================================================================
+# Keyboard control
+# ============================================================================
+
+
+def init_keyboard_listener():
+    """Recording shortcuts, terminal-first so they work over SSH.
+
+    Whenever stdin is a TTY we use the stdlib :class:`TerminalKeyListener` directly rather
+    than upstream's pynput-first :func:`init_keyboard_listener`, whose global listener would
+    capture the workstation console instead of this (often SSH) terminal. With no TTY we defer
+    to upstream (pynput on a GUI, else headless no-op).
+    """
+    if not (sys.stdin is not None and sys.stdin.isatty()):
+        from lerobot.utils.keyboard_input import init_keyboard_listener as _upstream
+
+        return _upstream()
+
+    from lerobot.utils.keyboard_input import TerminalKeyListener, apply_recording_control
+
+    events = {"exit_early": False, "rerecord_episode": False, "stop_recording": False}
+
+    # n/r/q are the arrow/Esc equivalents that survive escape-sequence splitting over laggy
+    # SSH/VNC links. Case-insensitive so Shift+letter still works.
+    def on_key(name: str) -> None:
+        key = name.lower()
+        if key in ("right", "n"):
+            apply_recording_control("right", events)
+        elif key in ("left", "r"):
+            apply_recording_control("left", events)
+        elif key in ("esc", "q"):
+            apply_recording_control("esc", events)
+
+    listener = TerminalKeyListener(on_key)
+    listener.start()
+    logging.info(
+        "Keyboard control via terminal — keep this terminal focused: "
+        "Right/n = end episode early, Left/r = re-record, Esc/q = stop."
+    )
+    return listener, events
@@ -0,0 +1,21 @@
+# CloudXR device-profile overrides for the Isaac Teleop XR -> SO-101 example.
+#
+# Passed to isaacteleop's CloudXRLauncher as `env_config` (via
+# XRControllerConfig.cloudxr_env_file). Format: KEY=value, one per line; `#`
+# comments and blank lines ignored; $VARS / ~ expanded. See
+# isaacteleop/cloudxr/env_config.py::_load_env_file.
+#
+# Runtime-resolved keys (XR_RUNTIME_JSON, XRT_NO_STDIN, NV_CXR_RUNTIME_DIR,
+# NV_CXR_OUTPUT_DIR) are reserved and ignored if set here.
+
+# Transport profile the runtime advertises (CloudXR default: auto-webrtc).
+# "Quest3" also covers the Pico 4. Other values: auto-native, AppleVisionPro.
+NV_DEVICE_PROFILE=Quest3
+
+# Input device discovery channels (both default to true; pinned for clarity).
+NV_CXR_ENABLE_PUSH_DEVICES=true
+NV_CXR_ENABLE_TENSOR_DATA=true
+
+# Runtime logs to ~/.cloudxr/logs — helps debug connection issues
+# (e.g. "Failed to get OpenXR system: -35").
+NV_CXR_FILE_LOGGING=true
@@ -0,0 +1,40 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""NVIDIA Isaac Teleop teleoperators for LeRobot.
+
+Each input device is an :class:`IsaacTeleopTeleoperator` subclass: :class:`XRController`
+(XR/VR controller) and :class:`SO101LeaderArm` (back-drivable SO-101 leader arm) ship today.
+"""
+
+from .base import IsaacTeleopTeleoperator
+from .clutch import Clutch
+from .config_isaac_teleop import IsaacTeleopConfig, SO101LeaderArmConfig, XRControllerConfig
+from .teleop_so101_leader_arm import SO101LeaderArm, leader_joints_to_robot_action
+from .teleop_xr_controller import XRController
+from .xr_controller_processor import MapXRControllerActionToRobotAction
+
+__all__ = [
+    "Clutch",
+    "IsaacTeleopConfig",
+    "IsaacTeleopTeleoperator",
+    "MapXRControllerActionToRobotAction",
+    "SO101LeaderArm",
+    "SO101LeaderArmConfig",
+    "XRController",
+    "XRControllerConfig",
+    "leader_joints_to_robot_action",
+]
@@ -0,0 +1,282 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Shared base for NVIDIA Isaac Teleop-backed LeRobot teleoperators.
+
+Isaac Teleop is a multi-modal framework: a single ``TeleopSession`` can be driven by
+XR controllers, hand tracking, Manus gloves, etc. Each modality is a
+:class:`Teleoperator` subclass in its own ``teleop_<device>.py``.
+
+:class:`IsaacTeleopTeleoperator` owns what those devices share — the session
+lifecycle, the per-step staleness/worker-health guard, and the no-op calibration
+tracking devices need. A concrete device implements :meth:`_build_pipeline` (its
+retargeting graph) and :meth:`get_action` (usually via :meth:`_step`).
+
+``isaacteleop`` is an optional NVIDIA dependency (install instructions in the example's
+``README.md``); its imports are guarded behind an availability check at module top, so this
+module imports without it and constructing a device fails fast with install instructions.
+"""
+
+from __future__ import annotations
+
+import abc
+import logging
+import os
+from collections.abc import Mapping
+from pathlib import Path
+from typing import TYPE_CHECKING, Any
+
+from lerobot.teleoperators.teleoperator import Teleoperator
+from lerobot.utils.import_utils import is_package_available
+
+from .config_isaac_teleop import IsaacTeleopConfig
+
+_isaacteleop_available = is_package_available("isaacteleop")
+
+if TYPE_CHECKING or _isaacteleop_available:
+    from isaacteleop.cloudxr import CloudXRLauncher
+    from isaacteleop.retargeting_engine.interface import (
+        ExecutionEvents,
+        ExecutionState,
+        GraphExecutable,
+        RetargeterIO,
+    )
+    from isaacteleop.teleop_session_manager import TeleopSession, TeleopSessionConfig
+else:
+    CloudXRLauncher = None
+    ExecutionEvents = None
+    ExecutionState = None
+    GraphExecutable = None
+    RetargeterIO = None
+    TeleopSession = None
+    TeleopSessionConfig = None
+
+logger = logging.getLogger(__name__)
+
+# Gripper closedness [0, 1] -> SO-101 follower motor units [0, 100] (RANGE_0_100, 100 = OPEN).
+# Shared by the XR processor and leader device, which invert via ``pos = (1 - c) * SCALE``.
+_GRIPPER_MOTOR_SCALE = 100.0
+
+
+def _require_isaacteleop() -> None:
+    """Fail fast with install pointers when the optional ``isaacteleop`` package is missing."""
+    if not _isaacteleop_available:
+        raise ImportError(
+            "The 'isaacteleop' package is required for Isaac Teleop devices but is not "
+            "installed. See examples/isaac_teleop_to_so101/README.md for install instructions."
+        )
+
+
+class IsaacTeleopTeleoperator(Teleoperator):
+    """Abstract base for teleoperators backed by an Isaac Teleop ``TeleopSession``.
+
+    Owns the session lifecycle and the per-step health guard; subclasses supply
+    :meth:`_build_pipeline` and :meth:`get_action`.
+    """
+
+    config_class = IsaacTeleopConfig
+
+    def __init__(self, config: IsaacTeleopConfig):
+        _require_isaacteleop()
+        super().__init__(config)
+        self.config: IsaacTeleopConfig = config
+        self._session: TeleopSession | None = None
+        self._cloudxr_launcher: CloudXRLauncher | None = None
+
+    # ------------------------------------------------------------------
+    # Pipeline construction (device override point)
+    # ------------------------------------------------------------------
+
+    @abc.abstractmethod
+    def _build_pipeline(self) -> GraphExecutable:
+        """Build this device's retargeting pipeline (the ``GraphExecutable`` for
+        ``TeleopSessionConfig.pipeline``). Called once in :meth:`connect`; its output
+        keys must match what :meth:`get_action` unpacks.
+        """
+        raise NotImplementedError
+
+    # ------------------------------------------------------------------
+    # Lifecycle (shared)
+    # ------------------------------------------------------------------
+
+    @property
+    def is_connected(self) -> bool:
+        return self._session is not None
+
+    @property
+    def is_calibrated(self) -> bool:
+        return True  # Tracking devices are self-calibrating.
+
+    def calibrate(self) -> None:
+        pass
+
+    def configure(self) -> None:
+        pass
+
+    def connect(self, calibrate: bool = True) -> None:
+        """Auto-launch the CloudXR runtime (unless opted out) and open the session.
+
+        The CloudXR launch blocks ~30s and, on the first run, prompts on stdin for the
+        EULA (accept once via ``python -m isaacteleop.cloudxr --accept-eula``). Opt out
+        when CloudXR runs externally via ``config.auto_launch_cloudxr=False`` or
+        ``LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1`` (env var wins).
+        """
+        if self._session is not None:
+            raise RuntimeError("Already connected. Call disconnect() first.")
+
+        self._ensure_cloudxr_runtime()
+
+        try:
+            pipeline = self._build_pipeline()
+            session_config = TeleopSessionConfig(app_name=self.config.app_name, pipeline=pipeline)
+            self._session = TeleopSession(session_config)
+            self._session.__enter__()
+        except Exception:
+            self._session = None
+            try:
+                self._stop_cloudxr_runtime()
+            except Exception:
+                logger.exception("Failed to stop CloudXR runtime during connect() rollback")
+            raise
+        logger.info("Isaac Teleop session started: %s", self.config.app_name)
+
+    def disconnect(self) -> None:
+        try:
+            if self._session is not None:
+                # Null the handle BEFORE __exit__: even a failed session teardown must not
+                # wedge the device as is_connected (blocking every later connect/disconnect).
+                session = self._session
+                self._session = None
+                session.__exit__(None, None, None)
+                logger.info("Isaac Teleop session ended")
+        finally:
+            # Reap the CloudXR runtime even if session teardown raised, and even if no
+            # session was ever established (e.g. the launcher came up but session creation
+            # failed before this point); a no-op when we never launched CloudXR (opt-out /
+            # externally-owned runtime), so we never stop a runtime we don't own.
+            self._stop_cloudxr_runtime()
+
+    # ------------------------------------------------------------------
+    # CloudXR runtime (shared)
+    # ------------------------------------------------------------------
+
+    def _ensure_cloudxr_runtime(self) -> None:
+        """Auto-launch the CloudXR runtime once, unless opted out.
+
+        Idempotent (no-op once the launcher is up). ``LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH``
+        is checked first and wins over ``config.auto_launch_cloudxr``. Constructing
+        :class:`CloudXRLauncher` mutates the process env (``XR_RUNTIME_JSON`` etc.) and
+        blocks until the runtime is ready or raises :class:`RuntimeError`.
+        """
+        if self._cloudxr_launcher is not None:
+            return
+
+        if os.environ.get("LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH", "").strip() == "1":
+            logger.info(
+                "LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1 set; skipping CloudXR auto-launch "
+                "(assuming CloudXR is already running externally)"
+            )
+            return
+
+        if not self.config.auto_launch_cloudxr:
+            logger.info(
+                "config.auto_launch_cloudxr is False; skipping CloudXR auto-launch "
+                "(assuming CloudXR is already running externally)"
+            )
+            return
+
+        logger.info("Launching CloudXR runtime (first run may prompt for EULA and take ~30s)...")
+
+        self._cloudxr_launcher = CloudXRLauncher(
+            install_dir=str(Path.home() / ".cloudxr"),
+            env_config=self.config.cloudxr_env_file,
+            accept_eula=False,
+        )
+
+    def _stop_cloudxr_runtime(self) -> None:
+        """Stop the auto-launched CloudXR runtime, if any.
+
+        Clean stop nulls the handle. On :class:`RuntimeError` the handle is RETAINED so
+        the launcher's ``atexit`` hook owns the retry — a later :meth:`connect` then
+        treats the retained runtime as still up and will not relaunch.
+        """
+        if self._cloudxr_launcher is None:
+            return
+        try:
+            self._cloudxr_launcher.stop()
+        except RuntimeError:
+            logger.warning("CloudXR runtime could not be terminated; handle retained for atexit cleanup")
+        else:
+            self._cloudxr_launcher = None
+            logger.info("CloudXR runtime stopped")
+
+    def send_feedback(self, feedback: dict[str, Any]) -> None:
+        pass  # Haptic feedback not yet implemented.
+
+    # ------------------------------------------------------------------
+    # Stepping (shared)
+    # ------------------------------------------------------------------
+
+    def _running_events(self) -> ExecutionEvents:
+        """Constant ``RUNNING`` ``ExecutionEvents`` for a device with no clutch lifecycle.
+
+        Keeps the stream flowing; ``reset`` stays ``False``. A clutched device that needs
+        a real lifecycle should build its own ``ExecutionEvents`` instead.
+        """
+        return ExecutionEvents(execution_state=ExecutionState.RUNNING, reset=False)
+
+    def _step(
+        self,
+        *,
+        execution_events: ExecutionEvents | None = None,
+        external_inputs: Mapping[str, Any] | None = None,
+    ) -> RetargeterIO:
+        """Step the session once and return the raw pipeline outputs.
+
+        Applies the shared guard: re-raises a retargeting-worker exception and warns on a
+        stale frame. Subclasses call this from :meth:`get_action`.
+
+        Args:
+            execution_events: The ``ExecutionEvents`` driving the session this frame.
+                Devices with a lifecycle (clutch) MUST pass this every frame — when
+                ``None``, ``TeleopSession.step`` auto-fires ``RUNNING`` (the clutch would
+                latch immediately and never stop).
+            external_inputs: Per-step inputs (e.g. a static ``base_T_anchor``) in the
+                ``{leaf_node_name: {output_port_name: TensorGroup}}`` shape ``step`` expects.
+
+        Raises:
+            RuntimeError: If not connected, or if the retargeting worker raised.
+        """
+        if self._session is None:
+            raise RuntimeError("Not connected. Call connect() first.")
+
+        result = self._session.step(
+            execution_events=execution_events,
+            external_inputs=external_inputs,
+        )
+
+        info = self._session.last_step_info
+        if info is not None:
+            if info.worker_exception is not None:
+                raise RuntimeError(
+                    "Isaac Teleop retargeting worker raised an exception"
+                ) from info.worker_exception
+            if info.frame_deadline_miss:
+                logger.warning(
+                    "Isaac Teleop frame deadline miss (returned_age_frames=%s)",
+                    info.returned_age_frames,
+                )
+        return result
@@ -0,0 +1,102 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Engage-relative clutch for the XR -> SO-101 teleop loop.
+
+Turns the raw controller grip pose into an absolute base-frame EE target, so the XR
+device can stay a thin raw-pose reader. Pure numpy + the local ``Rotation`` helper (no
+``isaacteleop``), so it is unit-testable without the XR runtime.
+"""
+
+from __future__ import annotations
+
+import numpy as np
+
+from lerobot.utils.rotation import Rotation
+
+
+class Clutch:
+    """Engage-relative clutch for both position AND orientation.
+
+    Latch an origin on engage, then track the base-frame delta from it, applied
+    independently to position and orientation. State:
+
+    - ``_last_commanded_pos`` / ``_last_commanded_rot``: last commanded EE pose; held
+      while disengaged so the arm freezes where it was left.
+    - ``_home_pos`` / ``_home_rot``: latched on engage — the EE pose the delta applies to.
+      The position comes from the arm's MEASURED pose when the caller provides it (so an
+      arm that moved while disengaged is not snapped back to a stale command); the
+      orientation always comes from the last commanded rotation (see NOTE below).
+    - ``_origin_pos`` / ``_origin_rot``: latched on engage — the controller pose the delta
+      is measured against.
+
+    Each engaged frame :meth:`rebase` returns::
+
+        pos = home_pos + (grip_pos - origin_pos)  # 1:1 controller -> EE translation
+        rot = (R_ctrl @ R_origin ^ -1) @ R_home  # base-frame delta, left-composed
+
+    On the engage edge the output is exactly the home pose (no teleport). The orientation
+    delta is left-composed (base frame), so hand rotation about base Z maps to EE rotation
+    about base Z. A re-clutch latches a fresh home/origin.
+
+    NOTE: ``_home_rot`` is the last *commanded* orientation even when the measured pose is
+    supplied: the 5-DOF SO-101 tracks orientation only softly, so its measured wrist
+    orientation persistently differs from the command, and latching the measurement would
+    inject that offset into the commanded signal on every re-clutch. Position has no such
+    tracking gap, and there latching the measurement is what prevents the snap-back.
+    """
+
+    def __init__(self, home_base_T_ee: np.ndarray):  # noqa: N803
+        # Seed the held pose from the arm's measured startup EE pose so the first
+        # engage latches home there (no jump on the first squeeze).
+        home = np.asarray(home_base_T_ee, dtype=float)
+        self._last_commanded_pos = home[:3, 3].copy()
+        self._last_commanded_rot = Rotation.from_matrix(home[:3, :3])
+        self._home_pos = self._last_commanded_pos.copy()
+        self._home_rot = self._last_commanded_rot
+        self._origin_pos = np.zeros(3, dtype=float)
+        self._origin_rot = Rotation.from_quat(np.array([0.0, 0.0, 0.0, 1.0]))
+
+    def engage(
+        self,
+        grip_pos: np.ndarray,
+        grip_quat: np.ndarray,
+        measured_base_T_ee: np.ndarray | None = None,  # noqa: N803
+    ) -> None:
+        """Latch the engage home (where the arm is now) and controller origin.
+
+        Pass ``measured_base_T_ee`` (FK of the measured joints) so the home POSITION is
+        where the arm physically is — if the arm moved while disengaged (gravity sag,
+        external contact), latching the stale last-commanded position would make the
+        first engaged frame command a full-speed jump back to it. The home ORIENTATION
+        always stays the last commanded one (see the class NOTE).
+        """
+        if measured_base_T_ee is not None:
+            self._home_pos = np.asarray(measured_base_T_ee, dtype=float)[:3, 3].copy()
+        else:
+            self._home_pos = self._last_commanded_pos.copy()
+        self._home_rot = self._last_commanded_rot
+        self._origin_pos = np.asarray(grip_pos, dtype=float).copy()
+        self._origin_rot = Rotation.from_quat(np.asarray(grip_quat, dtype=float))
+
+    def rebase(self, grip_pos: np.ndarray, grip_quat: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+        """Return the absolute base-frame EE target ``(pos [m], quat [xyzw])`` for this frame."""
+        pos = self._home_pos + (np.asarray(grip_pos, dtype=float) - self._origin_pos)
+        rot_ctrl = Rotation.from_quat(np.asarray(grip_quat, dtype=float))
+        rot = (rot_ctrl * self._origin_rot.inv()) * self._home_rot
+        self._last_commanded_pos = pos.copy()
+        self._last_commanded_rot = rot
+        return pos, rot.as_quat()
@@ -0,0 +1,135 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Configuration dataclasses for NVIDIA Isaac Teleop-backed teleoperators.
+
+:class:`IsaacTeleopConfig` holds the shared fields; each device adds its own subclass
+(e.g. :class:`XRControllerConfig`, :class:`SO101LeaderArmConfig`).
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import ClassVar
+
+from lerobot.teleoperators.config import TeleoperatorConfig
+
+
+@dataclass(kw_only=True)
+class IsaacTeleopConfig(TeleoperatorConfig):
+    """Shared config for all Isaac Teleop-backed teleoperators.
+
+    Uses its own draccus ``_choice_registry`` (decoupled from the global
+    :class:`TeleoperatorConfig` one) so ``--teleop.type`` on a field typed
+    ``IsaacTeleopConfig`` resolves against ONLY the Isaac devices — letting them claim
+    short names (``xr_controller``, ``so101_leader``) without colliding with the global
+    registry. These devices are selected by the example scripts, not routed through
+    ``make_teleoperator_from_config``.
+    """
+
+    _choice_registry: ClassVar[dict] = {}
+
+    app_name: str = "LeTeleop"
+    """Application name for the OpenXR / Isaac Teleop session."""
+
+    auto_launch_cloudxr: bool = True
+    """Auto-launch the CloudXR runtime on :meth:`connect`. Set ``False`` (or export
+    ``LEROBOT_CLOUDXR_SKIP_AUTOLAUNCH=1``, which wins) when CloudXR runs externally.
+    """
+
+    cloudxr_env_file: str | None = None
+    """Optional CloudXR device-profile ``.env`` (an INPUT profile selecting the headset
+    transport) passed to ``CloudXRLauncher``. ``None`` keeps the default auto-WebRTC profile.
+    """
+
+
+# Static rebase from the OpenXR controller anchor frame (X=Right, Y=Up, Z=Backward) into the
+# robot base frame (X=Forward, Y=Left, Z=Up). A proper rotation (det=+1): controller motion
+# forward -> robot +X, right -> robot -Y (i.e. rightward), up -> robot +Z.
+_DEFAULT_BASE_T_ANCHOR: list[list[float]] = [
+    [0.0, 0.0, -1.0, 0.0],
+    [-1.0, 0.0, 0.0, 0.0],
+    [0.0, 1.0, 0.0, 0.0],
+    [0.0, 0.0, 0.0, 1.0],
+]
+
+
+@IsaacTeleopConfig.register_subclass("xr_controller")
+@dataclass(kw_only=True)
+class XRControllerConfig(IsaacTeleopConfig):
+    """Config for Isaac Teleop XR (VR) controller teleoperation.
+
+    Exposes the raw base-frame grip pose, squeeze, and trigger via ``ControllersSource``.
+    No retargeters: the clutch and gripper mapping live in the owning loop.
+    """
+
+    hand_side: str = "right"
+    """Which controller hand to use: ``"left"`` or ``"right"``. A plain ``str`` (validated in
+    ``__post_init__``) because draccus cannot decode ``Literal``-typed fields from the CLI."""
+
+    clutch_threshold: float = 0.5
+    """Squeeze value above which the owning loop's clutch engages (held-to-enable). The
+    device reports only the raw squeeze; the threshold is applied by the loop."""
+
+    base_T_anchor: list[list[float]] = field(  # noqa: N815  (frameA_T_frameB transform-matrix convention)
+        # Fresh copy per instance: returning the module-level list itself would alias one
+        # mutable matrix across every config.
+        default_factory=lambda: [row.copy() for row in _DEFAULT_BASE_T_ANCHOR]
+    )
+    """Static 4x4 [row-major] transform rebasing the OpenXR controller anchor frame into
+    the robot base frame. Defaults to OpenXR (X=Right, Y=Up, Z=Backward) -> robot
+    (X=Forward, Y=Left, Z=Up). Plain nested lists so the config stays serializable.
+    """
+
+    def __post_init__(self):
+        if self.hand_side not in ("left", "right"):
+            raise ValueError(f"hand_side must be 'left' or 'right', got {self.hand_side!r}")
+
+
+# Provisional gripper open/close endpoints [rad], normalizing the streamed gripper angle
+# into the follower's RANGE_0_100 jaw target. Derived from the so101_leader plugin README's
+# example calibration (home_ticks=2048, range 2000..3000; angle = (ticks-home)*2*pi/4096).
+_DEFAULT_GRIPPER_OPEN_RAD = -0.074
+_DEFAULT_GRIPPER_CLOSE_RAD = 1.460
+
+
+@IsaacTeleopConfig.register_subclass("so101_leader")
+@dataclass(kw_only=True)
+class SO101LeaderArmConfig(IsaacTeleopConfig):
+    """Config for an Isaac Teleop SO-101 *leader arm* (generic joint-space device).
+
+    Mirrors the leader's joint angles 1:1 onto a follower SO-101. The leader state is
+    streamed in radians by the native ``so101_leader`` plugin and read via a
+    ``JointStateSource``; the device converts arm joints to degrees and the gripper to the
+    follower's RANGE_0_100 jaw target (no IK/clutch/retargeter on the LeRobot side).
+    """
+
+    port: str = ""
+    """Serial port of the physical LEADER arm (e.g. ``/dev/ttyACM1``), forwarded to the
+    plugin (which reads the servos) when the example launches it. Empty -> the plugin runs
+    its synthetic trajectory."""
+
+    collection_id: str = "so101_leader"
+    """Tensor collection id the leader plugin pushes on; must match the running
+    ``so101_leader`` plugin (its second positional arg, default ``"so101_leader"``)."""
+
+    gripper_open_rad: float = _DEFAULT_GRIPPER_OPEN_RAD
+    """Leader gripper angle [rad] at fully OPEN -> follower jaw 100. Provisional default;
+    set from the plugin's ``calibrate`` subcommand. See ``_DEFAULT_GRIPPER_OPEN_RAD``."""
+
+    gripper_close_rad: float = _DEFAULT_GRIPPER_CLOSE_RAD
+    """Leader gripper angle [rad] at fully CLOSED -> follower jaw 0. Provisional default;
+    set from the plugin's ``calibrate`` subcommand. See ``_DEFAULT_GRIPPER_CLOSE_RAD``."""
@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""SO-101 leader-arm device for NVIDIA Isaac Teleop, exposed to LeRobot.
+
+The leader is a back-drivable SO-101 whose six joint angles are streamed (in radians) by
+the native ``so101_leader`` plugin; this device reads them via a ``JointStateSource`` and
+converts them into follower-ready ``{joint}.pos``. Same kinematics as the follower, so it
+needs no retargeting — a 1:1 joint mirror, direct joint drive.
+
+Units (converted in the device so the output is always follower-valid):
+
+* arm joints: ``rad2deg`` — correct only if the leader's calibrated zero and the follower's
+  homing map to the same physical zero (the standard same-hardware assumption).
+* gripper: normalized from ``[gripper_open_rad, gripper_close_rad]`` to RANGE_0_100.
+
+``isaacteleop`` imports are guarded behind the availability flag so this module — and the
+pure :func:`leader_joints_to_robot_action` converter — import without it (construction
+fails fast via the base class).
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from lerobot.types import RobotAction
+
+from .base import _GRIPPER_MOTOR_SCALE, IsaacTeleopTeleoperator, _isaacteleop_available
+from .config_isaac_teleop import SO101LeaderArmConfig
+
+if TYPE_CHECKING or _isaacteleop_available:
+    from isaacteleop.retargeting_engine.deviceio_source_nodes import JointStateSource
+    from isaacteleop.retargeting_engine.interface import OutputCombiner
+else:
+    JointStateSource = None
+    OutputCombiner = None
+
+# Canonical SO-101 DOF names and order — matches the plugin stream and the follower's motor
+# order. Passed to the ``JointStateSource`` as its output layout; the source maps by name and
+# :func:`_joints_group_to_rad` reads back by name, so a layout mismatch can't mislabel a DOF.
+SO101_LEADER_JOINTS = [
+    "shoulder_pan",
+    "shoulder_lift",
+    "elbow_flex",
+    "wrist_flex",
+    "wrist_roll",
+    "gripper",
+]
+
+
+def leader_joints_to_robot_action(
+    joints_rad: dict[str, float],
+    *,
+    gripper_joint: str,
+    gripper_open_rad: float,
+    gripper_close_rad: float,
+) -> RobotAction:
+    """Convert streamed leader joint angles [rad] to follower-ready ``{joint}.pos``.
+
+    Pure (no ``isaacteleop``, no I/O). Iteration follows ``joints_rad`` insertion order, so
+    pass it in :data:`SO101_LEADER_JOINTS` order for a stable layout. Arm joints are
+    converted ``rad2deg``; ``gripper_joint`` is normalized from
+    ``[gripper_open_rad, gripper_close_rad]`` to RANGE_0_100 (clipped).
+    """
+    action: RobotAction = {}
+    span = gripper_close_rad - gripper_open_rad
+    for name, rad in joints_rad.items():
+        if name == gripper_joint:
+            # Closedness c=0 at open, c=1 at closed; invert to the follower's 100=open jaw.
+            closedness = 0.0 if span == 0.0 else (rad - gripper_open_rad) / span
+            closedness = min(1.0, max(0.0, closedness))
+            action[f"{name}.pos"] = (1.0 - closedness) * _GRIPPER_MOTOR_SCALE
+        else:
+            action[f"{name}.pos"] = float(np.rad2deg(rad))
+    return action
+
+
+def _joints_group_to_rad(joints) -> dict[str, float]:
+    """Read a ``JointStateSource`` output group into ``{joint_name: angle [rad]}``.
+
+    Pure (duck-typed on the group). The group is positional but each slot carries its joint
+    name in ``group.group_type.types``; we key off those names (not a positional index) so a
+    layout mismatch surfaces as a wrong/missing key here rather than a mislabeled DOF.
+    """
+    names = [t.name for t in joints.group_type.types]
+    return {name: float(joints[i]) for i, name in enumerate(names)}
+
+
+class SO101LeaderArm(IsaacTeleopTeleoperator):
+    """SO-101 leader-arm teleoperator (joint-space), direct joint mirror to the follower.
+
+    Reads the six joint angles off a single ``JointStateSource`` each frame; no retargeter,
+    no clutch. When the leader is not streaming, :meth:`get_action` returns the held-last
+    joints and :attr:`is_tracking` is ``False`` so the owning loop can hold the follower.
+    """
+
+    config_class = SO101LeaderArmConfig
+    name = "isaac_teleop_so101_leader"
+
+    def __init__(self, config: SO101LeaderArmConfig):
+        super().__init__(config)
+        self.config: SO101LeaderArmConfig = config
+        # Held-last joint angles [rad], seeded at zero (URDF/home pose) so the first frames
+        # before the plugin starts pushing read as the home pose, not garbage.
+        self._last_joints_rad: dict[str, float] = dict.fromkeys(SO101_LEADER_JOINTS, 0.0)
+        # Whether the most recent get_action() read live leader data (vs held-last).
+        self._is_tracking = False
+
+    # ------------------------------------------------------------------
+    # Pipeline construction
+    # ------------------------------------------------------------------
+
+    def _build_pipeline(self) -> OutputCombiner:
+        """Build the joint-mirror pipeline: a single ``JointStateSource`` leaf that converts
+        the raw stream into a name-keyed joint group. No retargeter (shared kinematics)."""
+        source = JointStateSource(
+            name="so101_leader",
+            collection_id=self.config.collection_id,
+            joint_names=SO101_LEADER_JOINTS,
+        )
+        return OutputCombiner({"joints": source.output(JointStateSource.JOINTS)})
+
+    # ------------------------------------------------------------------
+    # Action features
+    # ------------------------------------------------------------------
+
+    @property
+    def action_features(self) -> dict[str, type]:
+        # Matches the serial SOLeader's action features so this is a drop-in joint-space
+        # leader: one float `{joint}.pos` per DOF, sendable straight to an SO-101 follower.
+        return {f"{name}.pos": float for name in SO101_LEADER_JOINTS}
+
+    @property
+    def feedback_features(self) -> dict[str, type]:
+        return {}
+
+    @property
+    def is_tracking(self) -> bool:
+        """Whether the last :meth:`get_action` read live leader data (vs held-last)."""
+        return self._is_tracking
+
+    # ------------------------------------------------------------------
+    # Action extraction
+    # ------------------------------------------------------------------
+
+    def get_action(self) -> RobotAction:
+        """Step the session and return the leader joints as follower-ready ``{joint}.pos``.
+
+        When the leader is streaming, the live angles are cached and converted; otherwise the
+        held-last angles are reused and :attr:`is_tracking` is set ``False``.
+        """
+        result = self._step(execution_events=self._running_events())
+
+        joints = result["joints"]
+        # The JointStateSource output is Optional: absent (is_none) when the device is
+        # inactive. Treat that as "not tracking" and reuse the held-last angles.
+        self._is_tracking = not getattr(joints, "is_none", False)
+        if self._is_tracking:
+            try:
+                self._last_joints_rad = _joints_group_to_rad(joints)
+            except (AttributeError, IndexError, KeyError, TypeError, ValueError):
+                # A partially-populated / malformed group on an odd frame: keep held-last, but
+                # report it as not-tracking so the loop holds the follower rather than trusting it.
+                self._is_tracking = False
+
+        return leader_joints_to_robot_action(
+            self._last_joints_rad,
+            gripper_joint="gripper",
+            gripper_open_rad=self.config.gripper_open_rad,
+            gripper_close_rad=self.config.gripper_close_rad,
+        )
@@ -0,0 +1,204 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""XR (VR) controller device for NVIDIA Isaac Teleop, exposed to LeRobot.
+
+A deliberately thin reader: exposes the raw controller grip pose off
+``ControllersSource`` (statically rebased into the robot base frame by
+``ControllerTransform``), plus squeeze and trigger. No retargeters and no clutch —
+the clutch rebasing and gripper mapping live downstream in the owning loop, so this
+device is stateless across frames.
+
+``isaacteleop`` imports are guarded behind the availability flag so this module imports
+without it (construction fails fast via the base class).
+"""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+import numpy as np
+
+from lerobot.types import RobotAction
+
+from .base import IsaacTeleopTeleoperator, _isaacteleop_available
+from .config_isaac_teleop import XRControllerConfig
+
+if TYPE_CHECKING or _isaacteleop_available:
+    from isaacteleop.retargeting_engine.deviceio_source_nodes import ControllersSource
+    from isaacteleop.retargeting_engine.interface import OutputCombiner, TensorGroup, ValueInput
+    from isaacteleop.retargeting_engine.tensor_types import TransformMatrix
+    from isaacteleop.retargeting_engine.tensor_types.indices import ControllerInputIndex
+else:
+    ControllersSource = None
+    OutputCombiner = None
+    TensorGroup = None
+    ValueInput = None
+    TransformMatrix = None
+    ControllerInputIndex = None
+
+# Source-node name for the static base_T_anchor rebase input fed via
+# ``TeleopSession.step(external_inputs=...)`` each frame.
+_BASE_T_ANCHOR_INPUT = "base_T_anchor"
+
+
+class XRController(IsaacTeleopTeleoperator):
+    """Raw XR controller grip-pose teleoperator (base-frame), no retargeters.
+
+    Reads the raw grip pose + squeeze + trigger off a ``ControllersSource`` rebased into
+    the robot base frame. :meth:`get_action` returns the absolute base-frame grip pose
+    untouched; the owning loop owns the clutch and gripper mapping.
+    """
+
+    config_class = XRControllerConfig
+    name = "isaac_teleop_controller"
+
+    def __init__(self, config: XRControllerConfig):
+        super().__init__(config)
+        self.config: XRControllerConfig = config
+
+        # Constant base_T_anchor input, built once in connect() (a TensorGroup is heavy and
+        # isaacteleop-backed) and reused every step.
+        self._external_inputs: dict[str, Any] | None = None
+        # Whether the last get_action() read a tracked controller; the owning loop polls this
+        # to wait for the operator to connect before driving the arm.
+        self._is_tracking = False
+
+    # ------------------------------------------------------------------
+    # Pipeline construction
+    # ------------------------------------------------------------------
+
+    def _build_pipeline(self) -> OutputCombiner:
+        """Build the raw-grip-pose pipeline: a ``ControllersSource`` rebased into the base
+        frame by ``ControllerTransform``, exposed verbatim as ``"controller"``. No retargeters.
+        """
+        side = self.config.hand_side
+        controller_key = f"controller_{side}"
+
+        controllers = ControllersSource(name="controllers")
+        # Static base_T_anchor rebase fed via external_inputs each step.
+        xform = ValueInput(_BASE_T_ANCHOR_INPUT, TransformMatrix())
+        transformed = controllers.transformed(xform.output("value"))
+        ctrl = transformed.output(controller_key)
+
+        return OutputCombiner({"controller": ctrl})
+
+    def _build_external_inputs(self) -> dict[str, Any]:
+        """Materialize the constant ``base_T_anchor`` external input (once, in connect)."""
+        tg = TensorGroup(TransformMatrix())
+        tg[0] = np.asarray(self.config.base_T_anchor, dtype=np.float32)
+        return {_BASE_T_ANCHOR_INPUT: {"value": tg}}
+
+    def connect(self, calibrate: bool = True) -> None:
+        super().connect(calibrate=calibrate)
+        try:
+            self._external_inputs = self._build_external_inputs()
+        except Exception:
+            # Roll the session/runtime back so a failed connect() leaves no half-state
+            # (a live session behind a raised connect would leak the CloudXR runtime).
+            self.disconnect()
+            raise
+
+    # ------------------------------------------------------------------
+    # Action features
+    # ------------------------------------------------------------------
+
+    @property
+    def action_features(self) -> dict:
+        return {
+            "grip_pos": {
+                "dtype": "float32",
+                "shape": (3,),
+                "names": {"x": 0, "y": 1, "z": 2},
+            },
+            "grip_quat": {
+                "dtype": "float32",
+                "shape": (4,),
+                "names": {"qx": 0, "qy": 1, "qz": 2, "qw": 3},
+            },
+            # ``get_action`` returns scalars for these two, so the advertised
+            # shape is () (0-d) to stay consistent with the returned values.
+            "squeeze": {
+                "dtype": "float32",
+                "shape": (),
+                "names": None,
+            },
+            "trigger": {
+                "dtype": "float32",
+                "shape": (),
+                "names": None,
+            },
+        }
+
+    @property
+    def feedback_features(self) -> dict:
+        return {}
+
+    @property
+    def is_tracking(self) -> bool:
+        """Whether the last :meth:`get_action` read a tracked controller. ``False`` until the
+        headset is connected over CloudXR and its controllers are live; the owning loop polls
+        it to wait for the operator before commanding the arm."""
+        return self._is_tracking
+
+    # ------------------------------------------------------------------
+    # Action extraction
+    # ------------------------------------------------------------------
+
+    def get_action(self) -> RobotAction:
+        """Step the session and return the raw base-frame grip pose.
+
+        Reads the grip pose + squeeze + trigger off the transformed controller stream (with
+        the constant ``base_T_anchor`` rebase). When the controller is not tracked, returns
+        identity pose and squeeze/trigger = 0.0 so the owning loop freezes the arm.
+
+        Returns:
+            ``{"grip_pos": (3,) [m], "grip_quat": (4,) [qx,qy,qz,qw], "squeeze": float,
+            "trigger": float}`` — pose in the robot base frame; squeeze/trigger in ``[0, 1]``.
+        """
+        result = self._step(execution_events=self._running_events(), external_inputs=self._external_inputs)
+
+        # Optional controller group is None until the headset is connected and its controllers
+        # are live; expose that as is_tracking so the loop can wait before driving the arm.
+        controller = result["controller"]
+        grip_pos = np.zeros(3, dtype=np.float32)
+        grip_quat = np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float32)
+        squeeze = 0.0
+        trigger = 0.0
+        self._is_tracking = not getattr(controller, "is_none", False)
+        if self._is_tracking:
+            # Read ALL four fields into locals before committing any of them: a failure on a
+            # partially-populated frame must not mix live values with the safe defaults (a
+            # live squeeze paired with a defaulted trigger=0.0 would keep the clutch engaged
+            # while commanding the gripper fully open, dropping whatever is grasped). On
+            # failure the defaults stand untouched and the frame reports not-tracked.
+            try:
+                pos = np.asarray(controller[ControllerInputIndex.GRIP_POSITION], dtype=np.float32)
+                quat = np.asarray(controller[ControllerInputIndex.GRIP_ORIENTATION], dtype=np.float32)
+                squeeze_val = float(controller[ControllerInputIndex.SQUEEZE_VALUE])
+                trigger_val = float(controller[ControllerInputIndex.TRIGGER_VALUE])
+            except (IndexError, KeyError, TypeError, ValueError):
+                self._is_tracking = False
+            else:
+                grip_pos, grip_quat = pos, quat
+                squeeze, trigger = squeeze_val, trigger_val
+
+        return {
+            "grip_pos": grip_pos,
+            "grip_quat": grip_quat,
+            "squeeze": squeeze,
+            "trigger": trigger,
+        }
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Processor step that maps XR controller actions to robot EE targets.
+
+Analogous to ``MapPhoneActionToRobotAction``, this bridges the clutch-rebased EE pose to
+the IK pipeline's input contract (``EEBoundsAndSafety`` -> ``InverseKinematicsEEToJoints``).
+Pure (no ``isaacteleop``), so it is unit-testable without the XR runtime.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from lerobot.configs.types import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.processor import ProcessorStepRegistry, RobotActionProcessorStep
+from lerobot.types import RobotAction
+from lerobot.utils.rotation import Rotation
+
+from .base import _GRIPPER_MOTOR_SCALE
+
+
+@ProcessorStepRegistry.register("map_xr_controller_action_to_robot_action")
+@dataclass
+class MapXRControllerActionToRobotAction(RobotActionProcessorStep):
+    """Maps an absolute base-frame EE pose + gripper closedness to the IK input contract.
+
+    Pure, stateless rename (the owning loop's clutch already produced the absolute base-frame
+    target). Each frame it writes:
+
+    - ``ee.x/y/z`` = ``ee_pose[:3]`` (position [m]);
+    - ``ee.wx/wy/wz`` = rotvec of ``ee_pose[3:7]`` (orientation; the IK tracks it softly at a
+      small ``orientation_weight`` on the 5-DOF SO-101);
+    - ``ee.gripper_pos`` = ``(1 - closedness) * _GRIPPER_MOTOR_SCALE`` (jaw target [0, 100],
+      RANGE_0_100 where 100 = open, so closedness is inverted).
+
+    Input keys: ``ee_pose`` ``(7,)`` ``[x,y,z,qx,qy,qz,qw]``, ``closedness`` float in [0, 1].
+    """
+
+    def action(self, action: RobotAction) -> RobotAction:
+        ee_pose = action.pop("ee_pose")
+        closedness = float(action.pop("closedness"))
+
+        action["ee.x"] = float(ee_pose[0])
+        action["ee.y"] = float(ee_pose[1])
+        action["ee.z"] = float(ee_pose[2])
+        # Orientation target as a rotvec (quat [qx,qy,qz,qw] -> axis-angle); the IK
+        # consumes ee.w* as a rotvec and tracks it with orientation_weight.
+        rotvec = Rotation.from_quat(ee_pose[3:7]).as_rotvec()
+        action["ee.wx"] = float(rotvec[0])
+        action["ee.wy"] = float(rotvec[1])
+        action["ee.wz"] = float(rotvec[2])
+        # Inverted: closedness c=1 (closed) -> 0, c=0 (open) -> 100 (SO-101 calibration).
+        action["ee.gripper_pos"] = (1.0 - closedness) * _GRIPPER_MOTOR_SCALE
+        return action
+
+    def transform_features(
+        self, features: dict[PipelineFeatureType, dict[str, PolicyFeature]]
+    ) -> dict[PipelineFeatureType, dict[str, PolicyFeature]]:
+        for feat in ["ee_pose", "closedness"]:
+            features[PipelineFeatureType.ACTION].pop(feat, None)
+
+        for feat in [
+            "ee.x",
+            "ee.y",
+            "ee.z",
+            "ee.wx",
+            "ee.wy",
+            "ee.wz",
+            "ee.gripper_pos",
+        ]:
+            features[PipelineFeatureType.ACTION][feat] = PolicyFeature(type=FeatureType.ACTION, shape=(1,))
+
+        return features
@@ -0,0 +1,73 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Save the current SO-101 joint positions as the reset-origin pose (override).
+
+Move the arm to the desired reset pose by hand (torque off), then run this script to write
+those joints to a per-arm file in the LeRobot cache. ``teleoperate.py`` / ``record.py`` load
+it on startup (matched by ``--robot.id``) as the reset target instead of the defaults.
+
+Usage::
+
+    # 1. Move arm to desired reset pose by hand
+    python -m examples.isaac_teleop_to_so101.override_reset_pose [--port /dev/ttyACM0] [--id so101_follower_arm]
+
+    # 2. Launch teleop with the SAME --robot.id — it will now reset to this pose on startup
+    python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm --teleop.type=xr_controller
+"""
+
+import argparse
+import json
+from pathlib import Path
+
+from lerobot.robots.so_follower import SO100Follower, SO100FollowerConfig
+
+from .common import RESET_POSE_FILE
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
+    )
+    parser.add_argument("--port", type=str, default="/dev/ttyACM0")
+    parser.add_argument("--id", type=str, default="so101_follower_arm")
+    return parser.parse_args()
+
+
+def main():
+    args = parse_args()
+    robot = SO100Follower(SO100FollowerConfig(port=args.port, id=args.id, use_degrees=True))
+    robot.connect()
+    # Always disconnect the follower so a failure never leaks the serial connection.
+    try:
+        obs = robot.get_observation()
+        motor_names = list(robot.bus.motors.keys())
+        pose = {name: float(obs[f"{name}.pos"]) for name in motor_names}
+    finally:
+        robot.disconnect()
+
+    print("Current joint positions:")
+    for name, val in pose.items():
+        print(f"  {name:20s}: {val:.2f}")
+
+    reset_pose_file = Path(RESET_POSE_FILE.format(robot_name=robot.name, robot_id=robot.id))
+    reset_pose_file.parent.mkdir(parents=True, exist_ok=True)
+    reset_pose_file.write_text(json.dumps(pose, indent=2))
+    print(f"\nSaved to {reset_pose_file}")
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,321 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Record a LeRobot dataset via NVIDIA Isaac Teleop -> SO-101.
+
+Runs ``teleoperate.py``'s control loop while also saving each frame to a LeRobot dataset.
+``--teleop.type`` selects the device (``xr_controller`` | ``so101_leader``) as in
+``teleoperate.py``.
+
+Usage::
+
+    # XR (VR) controller: clutch + soft-orientation IK
+    python -m examples.isaac_teleop_to_so101.record \\
+        --robot.type=so101_follower \\
+        --robot.port=/dev/ttyACM0 \\
+        --robot.id=so101_follower_arm \\
+        --teleop.type=xr_controller \\
+        --robot.cameras="{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \\
+        --dataset.repo_id=<hf_user>/<dataset_name> \\
+        --dataset.single_task="Pick up vial from rack on the left side" \\
+        --dataset.num_episodes=3 \\
+        --dataset.episode_time_s=20 \\
+        --dataset.reset_time_s=5
+
+    # SO-101 leader arm: 1:1 joint mirror (real leader on /dev/ttyACM1)
+    python -m examples.isaac_teleop_to_so101.record \\
+        --robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm \\
+        --teleop.type=so101_leader --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \\
+        --launch_plugin=/path/to/IsaacTeleop/install/plugins/so101_leader/so101_leader_plugin \\
+        --dataset.repo_id=<hf_user>/<dataset_name> --dataset.single_task="Pick up the cube" \\
+        --dataset.num_episodes=3 --dataset.episode_time_s=20 --dataset.reset_time_s=5
+
+The loop/launch knobs mirror ``teleoperate.py`` (tagged ``[xr]`` / ``[leader]`` below).
+
+Keyboard shortcuts: Right/n = end episode early and save, Left/r = discard + re-record,
+Esc/q = stop after the current episode. All frames are recorded (including hold frames).
+"""
+
+import logging
+import time
+from dataclasses import asdict, dataclass
+from pprint import pformat
+
+from lerobot.cameras import CameraConfig  # noqa: F401
+from lerobot.cameras.opencv import OpenCVCameraConfig  # noqa: F401
+from lerobot.common.control_utils import sanity_check_dataset_robot_compatibility
+from lerobot.configs import parser
+from lerobot.configs.dataset import DatasetRecordConfig
+from lerobot.datasets import (
+    LeRobotDataset,
+    VideoEncodingManager,
+    aggregate_pipeline_dataset_features,
+    create_initial_features,
+    safe_stop_image_writer,
+)
+from lerobot.processor import make_default_processors
+from lerobot.robots import RobotConfig
+from lerobot.robots.so_follower import SOFollowerConfig  # noqa: F401  (registers so101_follower)
+from lerobot.utils.constants import ACTION, OBS_STR
+from lerobot.utils.feature_utils import build_dataset_frame, combine_feature_dicts
+from lerobot.utils.robot_utils import precise_sleep
+from lerobot.utils.utils import init_logging
+
+from .common import (
+    ALIGN_DURATION_S,
+    RESET_DURATION_S,
+    Device,
+    HoldLatch,
+    build_device,
+    init_keyboard_listener,
+)
+from .isaac_teleop import IsaacTeleopConfig
+
+
+@dataclass
+class RecordConfig:
+    """CLI config for Isaac Teleop -> SO-101 dataset recording.
+
+    ``--robot.*`` / ``--teleop.*`` / ``--dataset.*`` configure the follower, device, and
+    recording; the loop/launch knobs below carry the same ``[xr]`` / ``[leader]`` tags as
+    ``teleoperate.py``. Use ``--flag=false`` for booleans (draccus style).
+    """
+
+    robot: RobotConfig
+    # --teleop.type=xr_controller|so101_leader, resolved against IsaacTeleopConfig's registry.
+    teleop: IsaacTeleopConfig
+    dataset: DatasetRecordConfig
+
+    # [leader] Path to the so101_leader plugin binary to spawn after CloudXR is up (it then
+    # inherits the runtime env). None (default) -> assume the plugin already runs externally.
+    launch_plugin: str | None = None
+
+    # [xr] Slew all joints to the reset pose before the first episode (--reset_to_origin=false to
+    # keep the arm where it is). After the slew the clutch seeds its home from the measured pose.
+    reset_to_origin: bool = True
+    # [xr] Duration [s] of the reset-to-origin slew (passed through to setup_xr).
+    reset_duration: float = RESET_DURATION_S
+
+    # [leader] Slew the follower to the leader's first pose before mirroring (--align=false to
+    # begin the 1:1 mirror immediately; the follower may snap).
+    align: bool = True
+    # [leader] Duration [s] of the startup alignment slew.
+    align_duration: float = ALIGN_DURATION_S
+
+    # Resume recording on an existing (previously interrupted) dataset.
+    resume: bool = False
+
+
+@safe_stop_image_writer
+def _record_loop(
+    robot,
+    device: Device,
+    motor_names: list[str],
+    events: dict,
+    fps: int,
+    dataset: LeRobotDataset | None = None,
+    control_time_s: float = 0.0,
+    single_task: str | None = None,
+) -> None:
+    """Run one episode (or reset phase) of the control loop.
+
+    When ``dataset`` is None the loop still controls the robot (so the operator
+    can reposition the arm during the reset window) but does not record frames.
+    """
+    control_interval = 1.0 / fps
+    timestamp = 0.0
+    start_t = time.perf_counter()
+    record_frames = dataset is not None
+    hold = HoldLatch(motor_names)
+
+    while timestamp < control_time_s:
+        loop_start = time.perf_counter()
+
+        if events["exit_early"]:
+            events["exit_early"] = False
+            break
+
+        obs = robot.get_observation()
+
+        if record_frames:
+            observation_frame = build_dataset_frame(dataset.features, obs, prefix=OBS_STR)
+
+        # Device idle (XR clutch disengaged, or leader stream stale) -> hold the pose
+        # latched on the active->idle edge.
+        action = hold.resolve(device.compute(obs), obs)
+
+        robot.send_action(action)
+
+        if record_frames:
+            action_frame = build_dataset_frame(dataset.features, action, prefix=ACTION)
+            dataset.add_frame({**observation_frame, **action_frame, "task": single_task})
+
+        dt_s = time.perf_counter() - loop_start
+        precise_sleep(max(control_interval - dt_s, 0.0))
+        timestamp = time.perf_counter() - start_t
+
+
+@parser.wrap()
+def record(cfg: RecordConfig) -> LeRobotDataset:
+    init_logging()
+    logging.info(pformat(asdict(cfg)))
+
+    # Connect the follower, build the selected Isaac device, and run its pre-loop startup
+    # (reset slew / leader align) — shared with teleoperate.py.
+    robot, device, motor_names = build_device(cfg)
+
+    # Build dataset feature spec.  The IK pipeline lives inside device.compute(), so the
+    # action features are exactly robot.action_features (joint positions in degrees).
+    teleop_proc, _, obs_proc = make_default_processors()
+    dataset_features = combine_feature_dicts(
+        aggregate_pipeline_dataset_features(
+            pipeline=teleop_proc,
+            initial_features=create_initial_features(action=robot.action_features),
+            use_videos=cfg.dataset.video,
+        ),
+        aggregate_pipeline_dataset_features(
+            pipeline=obs_proc,
+            initial_features=create_initial_features(observation=robot.observation_features),
+            use_videos=cfg.dataset.video,
+        ),
+    )
+
+    num_cameras = len(robot.cameras) if hasattr(robot, "cameras") else 0
+    image_writer_threads = cfg.dataset.num_image_writer_threads_per_camera * num_cameras
+
+    dataset: LeRobotDataset | None = None
+    listener = None
+    try:
+        if cfg.resume:
+            dataset = LeRobotDataset.resume(
+                cfg.dataset.repo_id,
+                root=cfg.dataset.root,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                rgb_encoder=cfg.dataset.rgb_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
+                encoder_threads=cfg.dataset.encoder_threads,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+                image_writer_processes=cfg.dataset.num_image_writer_processes if num_cameras > 0 else 0,
+                image_writer_threads=image_writer_threads if num_cameras > 0 else 0,
+            )
+            sanity_check_dataset_robot_compatibility(dataset, robot, cfg.dataset.fps, dataset_features)
+        else:
+            cfg.dataset.stamp_repo_id()
+            dataset = LeRobotDataset.create(
+                cfg.dataset.repo_id,
+                cfg.dataset.fps,
+                root=cfg.dataset.root,
+                robot_type=robot.name,
+                features=dataset_features,
+                use_videos=cfg.dataset.video,
+                image_writer_processes=cfg.dataset.num_image_writer_processes,
+                image_writer_threads=image_writer_threads,
+                batch_encoding_size=cfg.dataset.video_encoding_batch_size,
+                rgb_encoder=cfg.dataset.rgb_encoder,
+                depth_encoder=cfg.dataset.depth_encoder,
+                encoder_threads=cfg.dataset.encoder_threads,
+                streaming_encoding=cfg.dataset.streaming_encoding,
+                encoder_queue_maxsize=cfg.dataset.encoder_queue_maxsize,
+            )
+
+        listener, events = init_keyboard_listener()
+
+        loop_kwargs = {
+            "robot": robot,
+            "device": device,
+            "motor_names": motor_names,
+            "events": events,
+            "fps": cfg.dataset.fps,
+            "single_task": cfg.dataset.single_task,
+        }
+
+        with VideoEncodingManager(dataset):
+            recorded_episodes = 0
+            while recorded_episodes < cfg.dataset.num_episodes and not events["stop_recording"]:
+                logging.info(f"Recording episode {dataset.num_episodes}")
+                _record_loop(
+                    **loop_kwargs,
+                    dataset=dataset,
+                    control_time_s=cfg.dataset.episode_time_s,
+                )
+
+                # Reset window: give the operator time to reposition the scene.
+                # Skipped for the last episode (or if stop_recording was set).
+                if not events["stop_recording"] and (
+                    recorded_episodes < cfg.dataset.num_episodes - 1 or events["rerecord_episode"]
+                ):
+                    logging.info("Reset the environment")
+                    _record_loop(
+                        **loop_kwargs,
+                        dataset=None,
+                        control_time_s=cfg.dataset.reset_time_s,
+                    )
+
+                if events["rerecord_episode"]:
+                    logging.info("Re-record episode")
+                    events["rerecord_episode"] = False
+                    events["exit_early"] = False
+                    dataset.clear_episode_buffer()
+                    continue
+
+                dataset.save_episode()
+                recorded_episodes += 1
+
+    finally:
+        logging.info("Stop recording")
+
+        # Hardware teardown FIRST, each step guarded: the arm must be freed promptly (not
+        # after a potentially long finalize/encode), a cleanup failure must not skip the
+        # follower disconnect (which is what disables torque), and neither must prevent
+        # the dataset from being finalized below.
+        try:
+            device.cleanup()
+        except Exception:
+            logging.exception("Device cleanup failed")
+        try:
+            if robot.is_connected:
+                robot.disconnect()
+        except Exception:
+            logging.exception("Robot disconnect failed")
+
+        # Restore the terminal before the (potentially long) finalize/encode.
+        if listener is not None:
+            try:
+                listener.stop()
+            except Exception:
+                logging.exception("Keyboard listener stop failed")
+
+        if dataset is not None:
+            dataset.finalize()
+
+        if cfg.dataset.push_to_hub:
+            if dataset is not None and dataset.num_episodes > 0:
+                dataset.push_to_hub(tags=cfg.dataset.tags, private=cfg.dataset.private)
+            else:
+                logging.warning("No episodes saved — skipping push to hub")
+
+        logging.info("Exiting")
+
+    return dataset
+
+
+def main():
+    record()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+
+# Copyright 2026 NVIDIA Corporation and 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.
+
+"""Teleoperate an SO-101 follower arm via NVIDIA Isaac Teleop.
+
+``lerobot-teleoperate``-style CLI (draccus): ``--teleop.type`` selects the Isaac device
+(``xr_controller`` | ``so101_leader``), ``--robot.*`` the follower::
+
+    # XR (VR) controller: clutch + soft-orientation IK
+    python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower \
+        --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm --teleop.type=xr_controller
+
+    # SO-101 leader arm: 1:1 joint mirror (real leader on /dev/ttyACM1)
+    python -m examples.isaac_teleop_to_so101.teleoperate --robot.type=so101_follower \
+        --robot.port=/dev/ttyACM0 --robot.id=so101_follower_arm --teleop.type=so101_leader \
+        --teleop.port=/dev/ttyACM1 --teleop.id=so101_leader_arm \
+        --launch_plugin=/code/Teleop/install/plugins/so101_leader/so101_leader_plugin
+
+``--teleop.type`` resolves against the Isaac device registry (see :class:`IsaacTeleopConfig`),
+distinct from the serial ``so101_leader``. The pipelines, clutch/IK/align internals, and
+reset-pose behavior live in ``common.py``. Requires the ``isaacteleop`` package and an OpenXR
+runtime (install instructions in this folder's ``README.md``).
+"""
+
+import time
+from dataclasses import dataclass
+
+from lerobot.configs import parser
+from lerobot.robots import RobotConfig
+from lerobot.robots.so_follower import SOFollowerConfig  # noqa: F401  (registers so101_follower)
+from lerobot.utils.robot_utils import precise_sleep
+
+from .common import (
+    ALIGN_DURATION_S,
+    FPS,
+    RESET_DURATION_S,
+    HoldLatch,
+    build_device,
+)
+from .isaac_teleop import IsaacTeleopConfig
+
+
+@dataclass
+class TeleoperateConfig:
+    """``lerobot-teleoperate``-style CLI for the Isaac Teleop -> SO-101 example.
+
+    The fields below are the loop/launch knobs (not part of either device's config); the
+    ``[xr]`` / ``[leader]`` tags mark which device a knob applies to. Use ``--flag=false``
+    for booleans (draccus style).
+    """
+
+    # Isaac Teleop input device + its knobs (--teleop.type=xr_controller|so101_leader,
+    # then --teleop.<field>=...). Resolved against IsaacTeleopConfig's own choice registry.
+    teleop: IsaacTeleopConfig
+    # SO-101 FOLLOWER arm (--robot.type=so101_follower --robot.port=/dev/ttyACM0 --robot.id=...).
+    robot: RobotConfig
+
+    # [leader] Path to the so101_leader plugin binary to spawn AFTER CloudXR is up (it then
+    # inherits the runtime env). None (default) -> assume the plugin already runs externally.
+    # The leader's serial port is --teleop.port (forwarded to the plugin; empty -> synthetic).
+    launch_plugin: str | None = None
+
+    # [xr] Slew all joints to a default reset pose before the loop (--reset_to_origin=false to
+    # keep the arm where it is). After the slew the clutch seeds its home from the measured pose.
+    reset_to_origin: bool = True
+    # [xr] Duration [s] of the reset-to-origin slew.
+    reset_duration: float = RESET_DURATION_S
+
+    # [leader] Slew the follower to the leader's first pose before mirroring (--align=false to
+    # begin the 1:1 mirror immediately; the follower may snap).
+    align: bool = True
+    # [leader] Duration [s] of the startup alignment slew.
+    align_duration: float = ALIGN_DURATION_S
+
+
+@parser.wrap()
+def teleoperate(cfg: TeleoperateConfig):
+    robot, device, motor_names = build_device(cfg)
+    hold = HoldLatch(motor_names)
+    try:
+        while True:
+            t0 = time.perf_counter()
+            obs = robot.get_observation()
+            # Idle (compute() -> None) holds the pose latched on the active->idle edge.
+            action = hold.resolve(device.compute(obs), obs)
+            robot.send_action(action)
+            precise_sleep(max(1.0 / FPS - (time.perf_counter() - t0), 0.0))
+    except KeyboardInterrupt:
+        pass
+    finally:
+        # A failing device cleanup must not skip the follower disconnect (which is what
+        # disables torque on the arm).
+        try:
+            device.cleanup()
+        finally:
+            robot.disconnect()
+
+
+def main():
+    teleoperate()
+
+
+if __name__ == "__main__":
+    main()
@@ -25,7 +25,7 @@ discord = "https://discord.gg/s3KuuzsPFb"

 [project]
 name = "lerobot"
-version = "0.5.2"
+version = "0.6.1"
 description = "🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch"
 dynamic = ["readme"]
 license = { text = "Apache-2.0" }
@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

+import logging
 from dataclasses import dataclass, field
 from typing import Any

@@ -33,6 +34,8 @@ from lerobot.processor import (
 )
 from lerobot.utils.rotation import Rotation

+logger = logging.getLogger(__name__)
+

@ProcessorStepRegistry.register("ee_reference_and_delta")
@dataclass
@@ -194,11 +197,17 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
    Attributes:
        end_effector_bounds: A dictionary with "min" and "max" keys for position clipping.
        max_ee_step_m: The maximum allowed change in position (in meters) between steps.
+        raise_on_jump: When ``True`` (default) an over-limit per-frame step raises
+            ``ValueError`` (aborting the control loop). When ``False`` the step is
+            rate-limited to ``max_ee_step_m`` and a warning is logged instead — the
+            safer choice for live teleoperation, where a transient tracking glitch
+            should not crash the loop and leave the robot uncontrolled.
        _last_pos: Internal state storing the last commanded position.
    """

    end_effector_bounds: dict
    max_ee_step_m: float = 0.05
+    raise_on_jump: bool = True
    _last_pos: np.ndarray | None = field(default=None, init=False, repr=False)

    def action(self, action: RobotAction) -> RobotAction:
@@ -226,8 +235,19 @@ class EEBoundsAndSafety(RobotActionProcessorStep):
            dpos = pos - self._last_pos
            n = float(np.linalg.norm(dpos))
            if n > self.max_ee_step_m and n > 0:
+                # Clamp the step to the per-frame limit (rate-limit). The clamped
+                # value is computed either way; raise_on_jump only decides whether
+                # an over-limit step aborts the loop or is rate-limited + warned.
                pos = self._last_pos + dpos * (self.max_ee_step_m / n)
-                raise ValueError(f"EE jump {n:.3f}m > {self.max_ee_step_m}m")
+                if self.raise_on_jump:
+                    raise ValueError(f"EE jump {n:.3f}m > {self.max_ee_step_m}m")
+                logger.warning(
+                    "EE jump %.3fm > %.3fm; rate-limited to the per-frame step "
+                    "(likely a transient tracking glitch; if it recurs every frame "
+                    "the commanded target is systematically out of workspace).",
+                    n,
+                    self.max_ee_step_m,
+                )

        self._last_pos = pos

@@ -264,12 +284,18 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
        q_curr: Internal state storing the last joint positions, used as an initial guess for the IK solver.
        initial_guess_current_joints: If True, use the robot's current joint state as the IK guess.
            If False, use the solution from the previous step.
+        orientation_weight: Weight for the orientation constraint passed to
+            ``RobotKinematics.inverse_kinematics``. Defaults to ``0.01`` (matching the solver
+            default, so existing callers are unchanged). Set to ``0.0`` for position-only IK on
+            under-actuated arms; a small nonzero weight gives soft-orientation IK on the 5-DOF
+            SO-101, where the wrist tracks orientation only partially (position dominates).
    """

    kinematics: RobotKinematics
    motor_names: list[str]
    q_curr: np.ndarray | None = field(default=None, init=False, repr=False)
    initial_guess_current_joints: bool = True
+    orientation_weight: float = 0.01

    def action(self, action: RobotAction) -> RobotAction:
        x = action.pop("ee.x")
@@ -308,7 +334,9 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
        t_des[:3, 3] = [x, y, z]

        # Compute inverse kinematics
-        q_target = self.kinematics.inverse_kinematics(self.q_curr, t_des)
+        q_target = self.kinematics.inverse_kinematics(
+            self.q_curr, t_des, orientation_weight=self.orientation_weight
+        )
        self.q_curr = q_target

        # TODO: This is sentitive to order of motor_names = q_target mapping
@@ -30,13 +30,19 @@ This is a Gaussian Actor policy (Gaussian policy with a tanh squash) — the pol
 {% elif model_name == "eo1" %}
 [EO-1](https://huggingface.co/papers/2508.21112) is a Vision-Language-Action model for general robot control. It pairs a Qwen2.5-VL backbone for vision-language understanding with a continuous flow-matching action head that denoises action chunks.
 {% elif model_name == "groot" %}
-[GR00T N1.5](https://github.com/NVIDIA/Isaac-GR00T) is an open, cross-embodiment foundation model from NVIDIA for generalized humanoid robot reasoning and skills. It takes language and images as input and uses a flow-matching action transformer to predict actions conditioned on vision, language, and proprioception.
+[GR00T N1.7](https://github.com/NVIDIA/Isaac-GR00T) is an open, cross-embodiment foundation model from NVIDIA for generalized humanoid robot reasoning and skills. It uses a Cosmos-Reason2/Qwen3-VL backbone and a flow-matching action transformer to predict actions conditioned on vision, language, and proprioception.
 {% elif model_name == "multi_task_dit" %}
 [Multi-Task Diffusion Transformer (DiT)](https://huggingface.co/papers/2507.05331) extends Diffusion Policy with a large Diffusion Transformer and text + vision conditioning for multi-task robot learning. It supports both diffusion and flow-matching objectives and reaches high dexterity with only ~450M parameters.
 {% elif model_name == "wall_x" %}
 [WALL-OSS](https://huggingface.co/papers/2509.11766) is an open-source foundation model for embodied intelligence from XSquare Robot. Built on Qwen2.5-VL, it uses a tightly-coupled multimodal architecture with flow matching to unify semantic reasoning and high-frequency action generation for cross-embodiment control.
 {% elif model_name == "xvla" %}
 [X-VLA](https://huggingface.co/papers/2510.10274) is a soft-prompted, flow-matching Vision-Language-Action framework that treats each robot or hardware setup as a "task" encoded with a small set of learnable Soft Prompt embeddings, letting a single model reconcile diverse robot morphologies, sensors, and action spaces.
+{% elif model_name == "evo1" %}
+[EVO1](https://github.com/MINT-SJTU/Evo-1) is a Vision-Language-Action policy built around an InternVL3 backbone and a continuous flow-matching action head. It embeds camera images and the language instruction with InternVL3 and predicts future action chunks via flow matching.
+{% elif model_name == "fastwam" %}
+[FastWAM](https://arxiv.org/abs/2603.16666) is a World Action Model policy that keeps video world-modeling during training but predicts actions directly at inference time, initializing its visual world-model components from the Wan2.2 video-diffusion stack.
+{% elif model_name == "lingbot_va" %}
+[LingBot-VA](https://github.com/Robbyant/lingbot-va) is an autoregressive video-action world-model policy built on the Wan2.2 video-diffusion stack. It interleaves the prediction of future video latents and robot actions in a single autoregressive sequence, feeding observed keyframes back into its KV cache for closed-loop world modeling.
 {% else %}
 This is a **{{ model_name }}** policy trained with [LeRobot](https://github.com/huggingface/lerobot).
 {% endif %}
@@ -75,7 +81,10 @@ This policy has been trained and pushed to the Hub using [LeRobot](https://githu
  "groot": "groot",
  "xvla": "xvla",
  "multi_task_dit": "multi_task_dit",
-  "wall_x": "walloss"
+  "wall_x": "walloss",
+  "evo1": "evo1",
+  "fastwam": "fastwam",
+  "lingbot_va": "lingbot_va"
 } %}
 {% if policy_docs.get(model_name) %}Learn how to train and run it in the [LeRobot {{ model_name }} guide](https://huggingface.co/docs/lerobot/main/en/{{ policy_docs[model_name] }}), or browse the [full documentation](https://huggingface.co/docs/lerobot/index).
 {% else %}See the [full LeRobot documentation](https://huggingface.co/docs/lerobot/index).
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
    "(python_full_version >= '3.15' and platform_machine == 'AMD64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'x86_64' and sys_platform == 'linux')",
@@ -2127,16 +2127,16 @@ wheels = [

 [[package]]
 name = "hydra-core"
-version = "1.3.3"
+version = "1.3.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "antlr4-python3-runtime", marker = "sys_platform == 'linux'" },
    { name = "omegaconf", marker = "sys_platform == 'linux'" },
    { name = "packaging", marker = "sys_platform == 'linux'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/0d/0b/7c0d941311aadc6479ec01767edba9c8a07db1452685de3567ed3058d0c9/hydra_core-1.3.3.tar.gz", hash = "sha256:b7477ee21f08b62f71bf0126d44695c048dc7e9c0cc79e2d593b707cb1e44048", size = 3262532, upload-time = "2026-06-11T05:54:26.835Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/dd/220f0e91743136725352497e98540772a01fc7c3ab96ff16c3c74424e984/hydra_core-1.3.4.tar.gz", hash = "sha256:ad0f7b05a0242255a8984d5a4ed2f6847f7b783ed727368a2c0155ec52d6c34c", size = 3263348, upload-time = "2026-07-04T16:25:38.891Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/e5/57/4e39f85347f77144d2ad12e87d5df8fb8f17023f9bd9e8c6e903a128382c/hydra_core-1.3.3-py3-none-any.whl", hash = "sha256:cf349fc393f486f250e5825592c3d0a50c0af3effd726cf8dd5b637a7cb464e3", size = 154706, upload-time = "2026-06-11T05:54:24.917Z" },
+    { url = "https://files.pythonhosted.org/packages/ed/cd/a568610bafe991fdd3f628fb606316b3b2be52ded019284e895d9beb3a1e/hydra_core-1.3.4-py3-none-any.whl", hash = "sha256:e58683692904a09f1fdfffa1a9b86bfd94e215b59f1ee17e7cd7d92738090d33", size = 155478, upload-time = "2026-07-04T16:25:37.291Z" },
 ]

 [[package]]
@@ -2823,7 +2823,7 @@ wheels = [

 [[package]]
 name = "lerobot"
-version = "0.5.2"
+version = "0.6.1"
 source = { editable = "." }
 dependencies = [
    { name = "cmake" },
@@ -6934,15 +6934,15 @@ wheels = [

 [[package]]
 name = "uvicorn"
-version = "0.49.0"
+version = "0.50.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "click" },
    { name = "h11" },
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 [package.optional-dependencies]
Author	SHA1	Message	Date
CarolinePascal	565f158a40	feat(upgrade): adding new robots, teleoperators and sensors to the third party packages list	2026-07-16 14:56:32 +02:00
CarolinePascal	51945259e0	docs(third party): adding a small list of noticeable third party robots packages	2026-07-12 23:36:38 +02:00
Lior Ben Horin	e40b58a8df	Update GR00T 1.7 LIBERO checkpoints (#3961 ) Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-08 13:25:54 +02:00
Mishig	3e538352ca	Make doc builds faster (#3958 ) * Update doc build workflow: light installs, drop custom container * Keep the pin comment dependabot-compatible	2026-07-08 07:31:10 +02:00
Steven Palma	8a74e0ac6d	chore(dependencies): Bump lerobot to 0.6.1 (#3957 )	2026-07-06 12:52:39 +02:00
Steven Palma	30da8e687a	chore(dependencies): Bump lerobot to 0.6.0 (#3956 )	2026-07-06 12:06:51 +02:00
Steven Palma	93257e3468	chore(dependencies): update uv.lock (#3928 )	2026-07-06 11:21:38 +02:00
Caroline Pascal	b895ed0fe4	docs(prettier): making video encoding parameters docs prettier (#3911 ) * docs(prettier): making video encoding parameters docs prettier * chore(format): formatting code * chore(contrast): removing poor contrast elements	2026-07-05 23:39:05 +02:00
Caroline Pascal	293a8d9a77	feat(examples): add Isaac Teleop → SO-101 teleoperation and dataset recording example (#3927 ) * Add Isaac Teleop SO-101 leader-arm teleoperator Add the NVIDIA Isaac Teleop teleoperator scaffolding and its first device: SO101LeaderArm, a back-drivable SO-101 leader arm on Isaac Teleop's generic joint-space device path. It reads the leader's joints from the so101_leader plugin via JointStateSource and emits follower-ready {joint}.pos (rad2deg arm, gripper -> RANGE_0_100) for direct 1:1 joint drive. - IsaacTeleopTeleoperator base + IsaacTeleopConfig (shared session/CloudXR config) - SO101LeaderArm / SO101LeaderArmConfig and leader_joints_to_robot_action - examples/isaac_teleop_to_so101/teleoperate_leader.py example - pure-numpy conversion tests - isaac-teleop optional extra + NVIDIA PyPI index in pyproject * Add Isaac Teleop XR controller teleoperator for SO-101 Add end-to-end XR (VR) controller teleoperation of an SO-101 follower arm via the NVIDIA Isaac Teleop stack, layered on the Isaac Teleop scaffolding. Teleoperator (src/lerobot/teleoperators/isaac_teleop/): - XRController / XRControllerConfig: connect to the CloudXR runtime, auto-launch the Isaac Teleop session, and expose get_action() emitting the raw base-frame grip pose, squeeze, and trigger. - MapXRControllerActionToRobotAction: stateless per-frame mapper from the XR action to the IK input contract (absolute ee.x/y/z, ee.gripper_pos, wrist_roll). - OverwriteWristRollFromAngle: post-IK step writing the operator wrist-roll [rad] onto wrist_roll.pos [deg], recovering the under-determined roll DOF. Example (examples/isaac_teleop_to_so101/): - teleoperate.py: thin absolute-pose IK pipeline with an in-loop clutch (engage latch + 1:1 delta rebase of position and orientation), EEBoundsAndSafety, and InverseKinematicsEEToJoints; slews to a recorded home on startup. - record_reset_pose.py / download_assets.py / webxr.env / .gitignore. Also: - Extend robot_kinematic_processor.py with EEBoundsAndSafety and InverseKinematicsEEToJoints. - Add XRControllerConfig + base_T_anchor to the Isaac Teleop config. - Add docs/source/isaac_teleop.mdx and the _toctree entry. - Add unit tests for the CloudXR launcher and the XR controller processor. * Unify Isaac Teleop SO-101 scripts behind a mandatory device selector Merge teleoperate.py (XR controller: clutch + soft-orientation IK) and teleoperate_leader.py (SO-101 leader arm: 1:1 joint mirror) into a single teleoperate.py driven by a `lerobot-teleoperate`-style draccus CLI: a follower `--robot.` and an input `--teleop.`, where `--teleop.type` (xr_controller \| so101_leader) selects the Isaac device. Uses a "dispatch, don't merge" shape: per-device setup_xr/setup_leader build a Device bundle (compute / startup / cleanup / command); a shared slew() takes a per-step target callable (XR a fixed reset pose, leader a live re-read so the 1:1 handoff stays continuous); one device-branchless outer loop runs both, with compute() -> None meaning "hold at the measured pose" (XR disengaged or leader stale). The entrypoint is @parser.wrap()'d over a TeleoperateConfig dataclass and dispatches on the parsed config type; device knobs ride on --teleop.* (the leader serial port is --teleop.port, forwarded to the plugin) and loop/launch knobs are top-level (--launch_plugin=<path> collapses the old --launch-plugin/--plugin-bin pair; --reset_to_origin/--align/--dry_run). To let the Isaac devices claim the natural --teleop.type names without colliding with the serial so101_leader of lerobot-teleoperate, give IsaacTeleopConfig its own draccus choice registry (own _choice_registry, decoupled from the global TeleoperatorConfig one) and register XRControllerConfig as "xr_controller" and SO101LeaderArmConfig as "so101_leader" there; the example types its teleop field as IsaacTeleopConfig so the choices resolve against that scoped registry. These devices drive the bespoke clutch/IK/align loop and are not routed through make_teleoperator_from_config, so dropping them from the global registry is inert. YAGNI sweep of the commit train: delete the orphaned OverwriteWristRollFromAngle (wrist_roll_processor.py) plus its export and tests -- no producer emits wrist_roll; the live XR path uses orientation-weight IK on the 5-DOF arm by design. Kept the load-bearing knobs (orientation_weight, raise_on_jump, base_T_anchor) and the optional reset-pose recorder. Updated isaac_teleop.mdx for the unified entrypoint and excised the stale roll-retargeter prose. Net LOC down (two scripts 714 lines -> one), in-loop device branches reduced to zero. Planned and reviewed via a 6-persona multi-agent panel (3-round planning convergence + 2-round review). Verification (isaacteleop/placo not installable here, so the device classes cannot connect, but their config dataclasses and the script import fine via deferred imports): the teleoperators test suite passes (45 passed, 2 skipped), draccus parsing of both target command lines yields the right config subclass with scoped --teleop.type, --help renders the scoped choices, the serial so101_leader stays in the global registry, and ruff check/format are green. Signed-off-by: Jiwen Cai <jiwenc@nvidia.com> * Add Isaac Teleop SO-101 dataset recording script record.py records a LeRobot dataset while driving the SO-101 follower with either Isaac Teleop device (--teleop.type=xr_controller \| so101_leader), mirroring teleoperate.py's device dispatch. * Extract shared Isaac Teleop SO-101 example infra into common.py teleoperate.py and record.py both built the per-device pipeline and ran the same read -> compute -> hold-when-idle -> sleep loop, with record.py importing internals from teleoperate.py. Move the shared device/loop infrastructure (Device, slew, Clutch, setup_xr/setup_leader + leader helpers, reset infra and constants) into a new common.py, and add build_device() + hold_action() to collapse the connect/dispatch/startup and idle-hold glue duplicated in both entry points. The setup functions now type their config against a LoopConfig Protocol, so common.py is decoupled from either CLI; both import from it. Also rename record_reset_pose.py -> override_reset_pose.py so it is not confused with record.py, and update the doc references. * Add stdin keyboard backend so recording shortcuts work over SSH/headless lerobot's init_keyboard_listener() uses pynput, which hooks GLOBAL key events from the display server. Over SSH, under Wayland, or on a headless box with only a TTY, keystrokes go to the terminal's stdin instead, so the listener never fires and the Right/Left/Esc recording shortcuts silently do nothing. Add a stdin (termios) keyboard backend to the example's common.py and an init_keyboard_listener() that prefers it whenever stdin is an interactive TTY (works over SSH / Wayland / headless-with-tty), falling back to lerobot's pynput/headless listener for GUI launches with no controlling terminal. Selectable via LEROBOT_KEYBOARD_BACKEND={auto,stdin,pynput,none}. The backend keeps ISIG so Ctrl-C still works and always restores the terminal (on stop() and via atexit). record.py now sources init_keyboard_listener from common; the Right/Left/Esc -> flag mapping and the (listener, events) contract are unchanged. Also convert record.py's loop_kwargs to a dict literal (ruff C408). * Wait for the XR headset to connect before driving the arm On the xr_controller path the example connected CloudXR and immediately ran the reset slew + control loop, even if no headset was connected — the arm moved before the operator was in VR, and get_action() just returned zeros so the clutch never engaged. Add an is_tracking property to XRController (set from the controller stream's optional group, mirroring SO101LeaderArm) and a _wait_for_xr_controller() helper in common.py that prints connection instructions (CloudXR web client URL + this workstation's candidate IPv4s, with loopback/link-local and virtual/bridge/USB-gadget interfaces filtered out) and polls until the controllers stream (indefinite, 15s reminder, Ctrl-C to abort). setup_xr.startup() now connects, waits for the headset, THEN runs the reset slew and seeds the clutch — so the arm only moves once the operator is connected and watching. Mirrors the leader path's _wait_for_leader; both record.py and teleoperate.py inherit it via the shared setup_xr. * Address review feedback on the Isaac Teleop -> SO-101 example Review-response and CI fixes for the Isaac Teleop -> SO-101 example. - Move the XR Clutch into src/lerobot/teleoperators/isaac_teleop/clutch.py (pure numpy + Rotation, no isaacteleop import), export it, and add tests/teleoperators/test_clutch.py. - Drop the vendored stdin keyboard listener; record.py uses a small terminal- first wrapper over upstream's TerminalKeyListener (works over SSH even with a local X display), falling back to upstream init_keyboard_listener otherwise. - record.py: pass rgb_encoder/depth_encoder to LeRobotDataset create()/resume() (upstream removed camera_encoder), fixing the AttributeError at record time. - build_device: derive motor names from robot.action_features instead of robot.bus (supports non-bus robots), and disconnect the follower if any step after connect() fails so a failed setup never leaks the connection. - Read leader joints by the group's declared names (_joints_group_to_rad) instead of positionally, so a layout mismatch can't silently mirror the wrong DOF onto the follower; add tests including a reversed-layout group. - base.py: hoist `from pathlib import Path` to module scope; only the isaacteleop CloudXRLauncher import stays lazy (optional dep). - Trim the common.py module docstring and point to docs/source/isaac_teleop.mdx. - default.env: correct the NV_DEVICE_PROFILE comment (auto-webrtc is the default; this file overrides to Quest3, which works for both Quest 3 and Pico 4). - download_assets.py: correct the RAW_BASE comment (tracks main, not pinned) and add `# nosec B310` next to the existing `# noqa: S310` for the bandit hook. - uv.lock: add the isaac-teleop extra's deps so `uv sync --locked` matches pyproject; regenerated with uv 0.8.0 to keep lockfile revision 2 (CI's uv). - isaac_teleop.mdx: prettier formatting. * fix(.gitignore): removing .gitignore and using lerobot cache folder instead to store local user files * chore(docstrings): reducing docstrings in default.env * feat(URDF): cleaning up and simplifying the URDF download procedure * feat(robot guard): adding a guard in case an unsupported robot type is provided (so-arms only) * fix(imports): enforcing a python module structure to simplify imports * feat(safe read): extending the motor bus safe read rationale to reset pose setting * chore(trim): trimming lenghty comments and docstrings * fix(deps): use isaacteleop [retargeters-lite] extra to unblock aarch64 (DGX Spark) (#3933) * fix(deps): drop isaacteleop [retargeters] extra to unblock aarch64 The [retargeters] extra pulls dex-retargeting (pins numpy<2.0, conflicting with lerobot's numpy>=2.0) and nlopt>=2.8 (no aarch64 wheels), making lerobot[isaac-teleop] unresolvable on ARM (DGX Spark, Jetson Thor, GH200) and over-constrained on numpy everywhere else. The LeRobot teleoperators only import isaacteleop.retargeting_engine, isaacteleop.cloudxr and isaacteleop.teleop_session_manager, all shipped in the base wheel (requires only numpy>=1.23), so the extra is unused. Verified on DGX Spark (aarch64, Python 3.12): resolves and installs with isaacteleop 1.3.131 + numpy 2.2.6; all imported symbols load. * fix(deps): use isaacteleop [retargeters-lite] extra for aarch64 support Pin to isaacteleop ~=1.3.131 (the release that added ARM64/aarch64 support) and swap the full [retargeters] extra for the new [retargeters-lite] one (scipy-only). The full extra drags in dex-retargeting (pins numpy<2, conflicting with lerobot's numpy>=2.0) and nlopt>=2.8 (no aarch64 wheels), making lerobot[isaac-teleop] unresolvable on ARM hosts (DGX Spark, Jetson Thor, GH200) and over-constrained on numpy everywhere else. The LeRobot teleoperators only import isaacteleop.retargeting_engine, isaacteleop.cloudxr and isaacteleop.teleop_session_manager — all covered by the base wheel + retargeters-lite. Verified on DGX Spark (aarch64, Python 3.12/3.13): resolves and installs with isaacteleop 1.3.131 + numpy 2.2.6 + scipy 1.18. * feat(deps): re-add full [retargeters] extra gated to x86_64 Keep the dex-retargeting/nlopt-based retargeters available on x86_64 (where their wheels exist) via an environment marker, while ARM hosts (DGX Spark, Jetson Thor, GH200) resolve with base + [retargeters-lite] only. Verified: uv lock resolves on both platforms; on aarch64 the compile excludes nlopt/dex-retargeting, on x86_64 they are included. --------- Co-authored-by: Johnny Nunez <22727137+johnnynunez@users.noreply.github.com> * chore(docstrings): trimming latest docstrings * chore(teleop): move isaac-teleop to examples + update docs + add readme with installation notes * chore(deps): restore uv.lock * fix(example: isaac teleop parsing config * fix(examples): isaac atomic-gripper controller * feat(Examples): isaac-teleop holdlatch * chore(examples): some other minor improvements for isaac-teleop * chore(examples): top-level imports isaac-teleop * chore(Examples): address ai review isaac-teleop --------- Signed-off-by: Jiwen Cai <jiwenc@nvidia.com> Co-authored-by: Jiwen Cai <jiwenc@nvidia.com> Co-authored-by: Johnny <johnnync13@gmail.com> Co-authored-by: Johnny Nunez <22727137+johnnynunez@users.noreply.github.com> Co-authored-by: Steven Palma <steven.palma@huggingface.co> Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>	2026-07-05 20:56:26 +02:00
Steven Palma	7957d4e2dc	chore(docs): update readme + gr00t libero results (#3941 ) * chore(docs): update readme + gr00t libero results * chore(docs): update template and in-tree policy steps	2026-07-05 15:11:46 +02:00