perf(examples): speed up slurm stats pre-submission on the login node

Resolve the source root via metadata only instead of instantiating a full LeRobotDataset (which memory-maps the entire frame index just to read a path), and download videos only when the run actually recomputes image/video stats (derived from --skip-image-video). This avoids the multi-TB video download for numeric-only runs.
feat(datasets): optionally rewrite per-episode stats when recomputing
2026-07-11 20:11:48 +00:00 · 2026-07-11 00:22:10 +02:00 · 2026-07-10 23:35:08 +02:00 · 2026-07-10 21:37:34 +02:00 · 2026-07-10 18:36:13 +02:00 · 2026-07-10 16:56:16 +02:00
26 changed files with 3733 additions and 114 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 }}
@@ -169,6 +169,8 @@
 - sections:
  - local: phone_teleop
    title: Phone
+  - local: isaac_teleop
+    title: Isaac Teleop
  title: "Teleoperators"
 - sections:
  - local: cameras
@@ -162,11 +162,11 @@ Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50`

 | Suite            | Success rate | Checkpoint                                                                                                    |
 | ---------------- | -----------: | ------------------------------------------------------------------------------------------------------------- |
-| LIBERO Spatial   |          91% | [nvidia/gr00t17-lerobot-libero_spatial-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_spatial-640) |
-| LIBERO Object    |          81% | [nvidia/gr00t17-lerobot-libero_object-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_object-640)   |
-| LIBERO Goal      |          97% | [nvidia/gr00t17-lerobot-libero_goal-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_goal-640)       |
-| LIBERO 10 (Long) |          84% | [nvidia/gr00t17-lerobot-libero_10-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_10-640)           |
-| **Average**      |   **88.25%** |                                                                                                               |
+| 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.
@@ -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 +1,489 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+SLURM-distributed recomputation of a LeRobotDataset's ``meta/stats.json``.
+
+Modified copy of lerobot's examples/dataset/slurm_recompute_stats.py
+(feat/recompute-stats-readonly-and-visual branch) with cluster-friendly additions:
+
+  1. --qos              : pass a SLURM QoS through to every worker's sbatch.
+  2. --venv-path        : activate a venv on each worker before the python step.
+  3. --env-command      : raw shell snippet injected before the python step (e.g. to
+                          export HF_LEROBOT_HOME). Runs in addition to --venv-path.
+  4. --chain-aggregate  : submit ``aggregate`` with an afterok dependency on
+                          ``compute`` so it only runs once all shards exist
+                          (no manual squeue-wait, no gap/overlap race).
+  5. --update-episode-stats : in ``aggregate``, also rewrite the per-episode stats in the
+                          episodes parquet so they stay consistent with meta/stats.json
+                          (default: only stats.json is written).
+
+Data access: no filesystem mount. Point HF_LEROBOT_HOME at a node-visible shared
+cache (e.g. /fsx/$USER/.cache) so the dataset downloads once and all workers read
+it. This is the download route; the source dataset is fetched from the Hub on the
+CPU workers.
+
+IMPORTANT — how to run (do NOT sbatch this file):
+  Run it as a normal python process on the LOGIN node. datatrove submits the
+  workers for you. The reference copy (--new-root) is built on the login node and
+  references the shared HF cache, so /fsx must be visible there (it is).
+
+Requires: pip install 'lerobot[dataset]' datatrove
+
+Example (single command, compute then dependent aggregate):
+
+    export HF_LEROBOT_HOME=/fsx/$USER/.cache
+
+    python slurm_recompute_stats_patched.py compute \
+        --repo-id behavior-1k/2026-challenge-demos \
+        --new-root /fsx/$USER/behavior-1k_recomputed \
+        --shard-dir /fsx/$USER/behavior-1k_recomputed/stats_shards \
+        --logs-dir /fsx/$USER/logs/recompute \
+        --skip-image-video 0 \
+        --workers 250 \
+        --partition hopper-cpu \
+        --qos normal \
+        --cpus-per-task 8 --mem-per-cpu 4G \
+        --venv-path /fsx/$USER/venvs/lerobot/bin/activate \
+        --env-command 'export HF_LEROBOT_HOME=/fsx/'"$USER"'/.cache' \
+        --chain-aggregate
+
+REHEARSE FIRST with --workers 2 --skip-image-video 1 and inspect one worker's log
+under --logs-dir to confirm QoS was accepted and a numeric stats.json is written.
+"""
+
+import argparse
+from pathlib import Path
+
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.executor.slurm import SlurmPipelineExecutor
+from datatrove.pipeline.base import PipelineStep
+
+class ComputeEpisodeStatsShards(PipelineStep):
+    """Each worker computes per-episode stats for its ``episodes[rank::world_size]`` shard."""
+
+    def __init__(self, repo_id, root, new_root, skip_image_video, shard_dir, video_backend=None):
+        super().__init__()
+        self.repo_id = repo_id
+        self.root = root
+        self.new_root = new_root
+        self.skip_image_video = skip_image_video
+        self.shard_dir = shard_dir
+        self.video_backend = video_backend
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        # NOTE: this method is pickled and executed on a worker, where this script's module
+        # globals are NOT available. Keep it self-contained: import locally and don't reference
+        # module-level helpers/constants.
+        import logging
+        import pickle
+        from pathlib import Path
+
+        from lerobot.datasets import LeRobotDataset, compute_dataset_episode_stats
+        from lerobot.utils.utils import init_logging
+
+        init_logging()
+        load_kwargs = {"video_backend": self.video_backend} if self.video_backend else {}
+        root = self.new_root if self.new_root and Path(self.new_root).exists() else self.root
+        dataset = LeRobotDataset(self.repo_id, root=root, **load_kwargs)
+
+        my_episodes = list(range(dataset.meta.total_episodes))[rank::world_size]
+        if not my_episodes:
+            logging.info(f"Rank {rank}: no episodes assigned")
+            return
+        logging.info(f"Rank {rank}: {len(my_episodes)} / {dataset.meta.total_episodes} episodes")
+
+        episode_stats = compute_dataset_episode_stats(
+            dataset,
+            episode_indices=my_episodes,
+            skip_image_video=self.skip_image_video,
+        )
+
+        shard_dir = Path(self.shard_dir)
+        shard_dir.mkdir(parents=True, exist_ok=True)
+        out = shard_dir / f"episode_stats_{rank:05d}.pkl"
+        with open(out, "wb") as f:
+            pickle.dump(episode_stats, f)
+        logging.info(f"Rank {rank}: saved {len(episode_stats)} episode stats to {out}")
+
+
+class AggregateEpisodeStats(PipelineStep):
+    """Merge all per-episode stat shards into meta/stats.json."""
+
+    def __init__(
+        self,
+        repo_id,
+        root,
+        new_root,
+        shard_dir,
+        push_to_hub=False,
+        video_backend=None,
+        update_episode_stats=False,
+    ):
+        super().__init__()
+        self.repo_id = repo_id
+        self.root = root
+        self.new_root = new_root
+        self.shard_dir = shard_dir
+        self.push_to_hub = push_to_hub
+        self.video_backend = video_backend
+        self.update_episode_stats = update_episode_stats
+
+    def run(self, data=None, rank: int = 0, world_size: int = 1):
+        # NOTE: pickled and executed on a worker; keep self-contained (see ComputeEpisodeStatsShards.run).
+        import logging
+        import pickle
+        from pathlib import Path
+
+        from lerobot.datasets import LeRobotDataset, aggregate_episode_stats
+        from lerobot.utils.utils import init_logging
+
+        init_logging()
+        if rank != 0:
+            return
+
+        shard_dir = Path(self.shard_dir)
+        shards = sorted(shard_dir.glob("episode_stats_*.pkl"))
+        if not shards:
+            raise FileNotFoundError(f"No episode stat shards found in {shard_dir}")
+
+        # Shards map episode_index -> stats; merging by key makes a dropped shard show up as a
+        # missing episode and a re-run shard overwrite rather than double-count.
+        all_episode_stats = {}
+        for shard in shards:
+            with open(shard, "rb") as f:
+                all_episode_stats.update(pickle.load(f))
+        logging.info(f"Aggregating {len(all_episode_stats)} episode stats from {len(shards)} shards")
+
+        load_kwargs = {"video_backend": self.video_backend} if self.video_backend else {}
+        root = self.new_root if self.new_root and Path(self.new_root).exists() else self.root
+        dataset = LeRobotDataset(self.repo_id, root=root, **load_kwargs)
+
+        # Aggregation is order-independent, so the only way sharding changes the result is a
+        # gap (dropped shard) or an overlap (episode counted twice). Verify the shards cover
+        # every episode exactly once before writing stats.json.
+        expected_episodes = dataset.meta.total_episodes
+        if len(all_episode_stats) != expected_episodes:
+            raise ValueError(
+                f"Expected {expected_episodes} per-episode stats (one per episode) but got "
+                f"{len(all_episode_stats)} across {len(shards)} shards. A compute shard is likely "
+                "missing or was written more than once; re-run the failed shards before aggregating."
+            )
+
+        # Frame-count check catches the case where a duplicate and a gap cancel out in the
+        # episode count: summed per-episode frame counts must equal the dataset's total frames.
+        stats_values = list(all_episode_stats.values())
+        numeric_key = next(
+            (
+                k
+                for k, v in dataset.meta.features.items()
+                if v["dtype"] not in ("image", "video", "string") and stats_values and k in stats_values[0]
+            ),
+            None,
+        )
+        if numeric_key is not None:
+            total_frames = sum(int(s[numeric_key]["count"][0]) for s in stats_values)
+            if total_frames != dataset.meta.total_frames:
+                raise ValueError(
+                    f"Summed frame count from shards ({total_frames}) != dataset total_frames "
+                    f"({dataset.meta.total_frames}); episodes are double-counted or missing."
+                )
+
+        new_stats = aggregate_episode_stats(
+            dataset, all_episode_stats, update_episode_stats=self.update_episode_stats
+        )
+        if new_stats is None:
+            raise RuntimeError("Aggregation produced no stats")
+        logging.info(f"Wrote stats for features: {list(new_stats.keys())} to {dataset.root}")
+
+        if self.push_to_hub:
+            logging.info(f"Pushing {self.repo_id} to hub")
+            dataset.push_to_hub()
+
+
+def _mem_gb(mem: str) -> int:
+    """Parse '4G' / '4GB' / '4' into an int number of GB for datatrove's mem_per_cpu_gb."""
+    s = str(mem).strip().lower().rstrip("b").rstrip("g")
+    return int(float(s))
+
+
+def _make_executor(
+    pipeline,
+    logs_dir,
+    job_name,
+    slurm,
+    workers,
+    tasks,
+    time,
+    partition,
+    cpus,
+    mem,
+    qos=None,
+    env_command=None,
+    venv_path=None,
+    depends=None,
+):
+    kwargs = {"pipeline": pipeline, "logging_dir": str(Path(logs_dir) / job_name)}
+    if slurm:
+        kwargs.update(
+            {
+                "job_name": job_name,
+                "tasks": tasks,
+                "workers": workers,
+                "time": time,
+                "partition": partition,
+                "cpus_per_task": cpus,
+                "mem_per_cpu_gb": _mem_gb(mem),  # datatrove's native field (int GB)
+                "sbatch_args": {},
+            }
+        )
+        if qos:
+            kwargs["qos"] = qos  # -> "#SBATCH --qos=<qos>" on every worker
+        if venv_path:
+            kwargs["venv_path"] = venv_path  # datatrove sources this before the python step
+        if env_command:
+            kwargs["env_command"] = env_command  # extra raw snippet before python (composes with venv_path)
+        if depends is not None:
+            kwargs["depends"] = depends  # chains --dependency=afterok:<compute jobid>
+        return SlurmPipelineExecutor(**kwargs)
+    kwargs.update({"tasks": tasks, "workers": 1})
+    return LocalPipelineExecutor(**kwargs)
+
+
+def _maybe_reference_copy(repo_id, root, new_root, download_videos):
+    """Create the read-only-safe reference copy once, before submitting workers.
+
+    Loads metadata only (to resolve the source root and revision) instead of a full
+    ``LeRobotDataset``, which would also memory-map the entire frame index just to read a
+    path. Fetches the source into the shared cache so the copy's symlinks point at real
+    files and workers don't each re-download, pulling videos only when the run needs them
+    (i.e. when image/video stats are being recomputed).
+    """
+    if not new_root:
+        return
+    from huggingface_hub import snapshot_download
+
+    from lerobot.datasets.dataset_metadata import LeRobotDatasetMetadata
+    from lerobot.scripts.lerobot_edit_dataset import _reference_copy_dataset
+    from lerobot.utils.constants import HF_LEROBOT_HUB_CACHE
+
+    new_root_path = Path(new_root)
+    if new_root_path.exists():
+        return
+
+    meta = LeRobotDatasetMetadata(repo_id, root=Path(root) if root else None)
+    ignore_patterns = None if download_videos else "videos/"
+    if root:
+        snapshot_download(
+            repo_id,
+            repo_type="dataset",
+            revision=meta.revision,
+            local_dir=meta.root,
+            ignore_patterns=ignore_patterns,
+        )
+        src_root = Path(meta.root)
+    else:
+        src_root = Path(
+            snapshot_download(
+                repo_id,
+                repo_type="dataset",
+                revision=meta.revision,
+                cache_dir=HF_LEROBOT_HUB_CACHE,
+                ignore_patterns=ignore_patterns,
+            )
+        )
+    _reference_copy_dataset(src_root, new_root_path)
+
+
+def _add_shared_args(p):
+    p.add_argument("--repo-id", type=str, required=True, help="Dataset identifier, e.g. 'user/dataset'.")
+    p.add_argument("--root", type=str, default=None, help="Source dataset root (defaults to the Hub cache).")
+    p.add_argument(
+        "--new-root",
+        type=str,
+        default=None,
+        help="Writable output root; a read-only-safe reference copy of --root. If omitted, stats "
+        "are written in place at --root.",
+    )
+    p.add_argument("--shard-dir", type=Path, default=Path("stats_shards"), help="Per-rank shard dir.")
+    p.add_argument("--logs-dir", type=Path, default=Path("logs"), help="datatrove logs dir.")
+    p.add_argument("--job-name", type=str, default=None, help="SLURM job name.")
+    p.add_argument("--slurm", type=int, default=1, help="1 = submit via SLURM; 0 = run locally.")
+    p.add_argument("--partition", type=str, default=None, help="SLURM partition, e.g. 'hopper-cpu'.")
+    p.add_argument("--qos", type=str, default=None, help="SLURM QoS, e.g. 'normal'. Passed to every worker.")
+    p.add_argument("--cpus-per-task", type=int, default=4, help="CPUs per SLURM task.")
+    p.add_argument("--mem-per-cpu", type=str, default="4G", help="Memory per CPU, e.g. '4G'.")
+    p.add_argument(
+        "--video-backend",
+        type=str,
+        default=None,
+        help="Video decoding backend (e.g. 'pyav', 'torchcodec'). Defaults to the dataset's default; "
+        "use 'pyav' if torchcodec fails to load locally.",
+    )
+    p.add_argument("--venv-path", type=str, default=None, help="venv activate script sourced on each worker.")
+    p.add_argument(
+        "--env-command",
+        type=str,
+        default=None,
+        help="Raw shell snippet injected into each worker's sbatch before the python step "
+        "(e.g. to export HF_LEROBOT_HOME). Runs in addition to --venv-path.",
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="PATCHED SLURM-distributed LeRobotDataset stats recomputation",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    cp = sub.add_parser("compute", help="Distribute per-episode stats across SLURM workers.")
+    _add_shared_args(cp)
+    cp.add_argument("--workers", type=int, default=50, help="Number of parallel SLURM tasks.")
+    cp.add_argument(
+        "--skip-image-video",
+        type=int,
+        default=1,
+        help="1 = numeric features only (fast); 0 = also recompute image/video stats (decodes frames).",
+    )
+    cp.add_argument(
+        "--chain-aggregate",
+        action="store_true",
+        help="After building compute, submit aggregate with an afterok dependency (single command).",
+    )
+    cp.add_argument("--push-to-hub", action="store_true", help="For the chained aggregate: push after done.")
+    cp.add_argument(
+        "--update-episode-stats",
+        action="store_true",
+        help="For the chained aggregate: also rewrite per-episode stats in the episodes parquet.",
+    )
+
+    ap = sub.add_parser("aggregate", help="Merge shards into meta/stats.json.")
+    _add_shared_args(ap)
+    ap.add_argument("--push-to-hub", action="store_true", help="Push the dataset after aggregation.")
+    ap.add_argument(
+        "--update-episode-stats",
+        action="store_true",
+        help="Also rewrite per-episode stats in the episodes parquet to match stats.json.",
+    )
+    ap.add_argument(
+        "--depends-job-id",
+        type=str,
+        default=None,
+        help="Optional SLURM job id; aggregate waits for it (afterok) before running.",
+    )
+
+    args = parser.parse_args()
+    slurm = args.slurm == 1
+
+    if args.command == "compute":
+        # The reference copy (if any) is created once on the submitting node so workers
+        # can all load --new-root without racing to build it. Videos are only fetched when
+        # image/video stats are being recomputed.
+        _maybe_reference_copy(
+            args.repo_id, args.root, args.new_root, download_videos=not bool(args.skip_image_video)
+        )
+
+        compute_exec = _make_executor(
+            pipeline=[
+                ComputeEpisodeStatsShards(
+                    args.repo_id,
+                    args.root,
+                    args.new_root,
+                    bool(args.skip_image_video),
+                    str(args.shard_dir),
+                    args.video_backend,
+                )
+            ],
+            logs_dir=args.logs_dir,
+            job_name=args.job_name or "recompute_stats_compute",
+            slurm=slurm,
+            workers=args.workers,
+            tasks=args.workers,
+            time="24:00:00",
+            partition=args.partition,
+            cpus=args.cpus_per_task,
+            mem=args.mem_per_cpu,
+            qos=args.qos,
+            env_command=args.env_command,
+            venv_path=args.venv_path,
+        )
+
+        if args.chain_aggregate and slurm:
+            # Build aggregate depending on compute. datatrove launches the dependency
+            # (compute) first, then submits aggregate with --dependency=afterok:<jobid>.
+            aggregate_exec = _make_executor(
+                pipeline=[
+                    AggregateEpisodeStats(
+                        args.repo_id,
+                        args.root,
+                        args.new_root,
+                        str(args.shard_dir),
+                        args.push_to_hub,
+                        args.video_backend,
+                        args.update_episode_stats,
+                    )
+                ],
+                logs_dir=args.logs_dir,
+                job_name="recompute_stats_aggregate",
+                slurm=slurm,
+                workers=1,
+                tasks=1,
+                time="02:00:00",
+                partition=args.partition,
+                cpus=args.cpus_per_task,
+                mem=args.mem_per_cpu,
+                qos=args.qos,
+                env_command=args.env_command,
+                venv_path=args.venv_path,
+                depends=compute_exec,
+            )
+            aggregate_exec.run()
+        else:
+            compute_exec.run()
+    else:
+        aggregate_exec = _make_executor(
+            pipeline=[
+                AggregateEpisodeStats(
+                    args.repo_id,
+                    args.root,
+                    args.new_root,
+                    str(args.shard_dir),
+                    args.push_to_hub,
+                    args.video_backend,
+                    args.update_episode_stats,
+                )
+            ],
+            logs_dir=args.logs_dir,
+            job_name=args.job_name or "recompute_stats_aggregate",
+            slurm=slurm,
+            workers=1,
+            tasks=1,
+            time="02:00:00",
+            partition=args.partition,
+            cpus=args.cpus_per_task,
+            mem=args.mem_per_cpu,
+            qos=args.qos,
+            env_command=args.env_command,
+            venv_path=args.venv_path,
+        )
+        if args.depends_job_id is not None:
+            aggregate_exec.depends_job_id = args.depends_job_id
+        aggregate_exec.run()
+
+
+if __name__ == "__main__":
+    main()
@@ -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" }
@@ -25,6 +25,8 @@ from .compute_stats import DEFAULT_QUANTILES, aggregate_stats, get_feature_stats
 from .dataset_metadata import CODEBASE_VERSION, LeRobotDatasetMetadata
 from .dataset_tools import (
    add_features,
+    aggregate_episode_stats,
+    compute_dataset_episode_stats,
    convert_image_to_video_dataset,
    delete_episodes,
    merge_datasets,
@@ -34,6 +36,7 @@ from .dataset_tools import (
    reencode_dataset,
    remove_feature,
    split_dataset,
+    write_episode_stats,
 )
 from .factory import make_dataset, make_train_eval_datasets, resolve_delta_timestamps
 from .image_writer import safe_stop_image_writer
@@ -78,8 +81,10 @@ __all__ = [
    "detect_available_encoders_pyav",
    "add_features",
    "aggregate_datasets",
+    "aggregate_episode_stats",
    "aggregate_pipeline_dataset_features",
    "aggregate_stats",
+    "compute_dataset_episode_stats",
    "convert_image_to_video_dataset",
    "create_initial_features",
    "compute_sampler_state",
@@ -99,5 +104,6 @@ __all__ = [
    "resolve_delta_timestamps",
    "safe_stop_image_writer",
    "split_dataset",
+    "write_episode_stats",
    "write_stats",
 ]
@@ -33,11 +33,13 @@ from pathlib import Path
 import datasets
 import numpy as np
 import pandas as pd
+import pyarrow as pa
 import pyarrow.parquet as pq
 import torch
 from tqdm import tqdm

 from lerobot.configs import (
+    DEFAULT_DEPTH_UNIT,
    DepthEncoderConfig,
    RGBEncoderConfig,
    VideoEncoderConfig,
@@ -52,10 +54,14 @@ from lerobot.utils.utils import flatten_dict
 from .aggregate import aggregate_datasets
 from .compute_stats import (
    aggregate_stats,
+    auto_downsample_height_width,
    compute_episode_stats,
    compute_relative_action_stats,
+    get_feature_stats,
+    sample_indices,
 )
 from .dataset_metadata import LeRobotDatasetMetadata
+from .depth_utils import dequantize_depth
 from .image_writer import write_image
 from .io_utils import (
    get_parquet_file_size_in_mb,
@@ -77,6 +83,7 @@ from .utils import (
    update_chunk_file_indices,
 )
 from .video_utils import (
+    decode_video_frames,
    encode_video_frames,
    reencode_video,
 )
@@ -1559,6 +1566,173 @@ def modify_tasks(
    return dataset


+def _load_episode_image_frames(
+    dataset: LeRobotDataset,
+    key: str,
+    ep_idx: int,
+    frame_offsets: list[int],
+    is_depth: bool,
+) -> np.ndarray:
+    """Load sampled frames of an image feature for one episode as a (N, C, H, W) array."""
+    ep = dataset.meta.episodes[ep_idx]
+    from_idx = ep["dataset_from_index"]
+    column = dataset.hf_dataset.with_format(None).select_columns(key)
+
+    frames = []
+    for offset in frame_offsets:
+        img = column[from_idx + offset][key]
+        if is_depth:
+            arr = np.array(img)
+            if arr.ndim == 2:
+                arr = arr[np.newaxis, ...]
+        else:
+            arr = np.transpose(np.array(img.convert("RGB"), dtype=np.uint8), (2, 0, 1))
+        frames.append(auto_downsample_height_width(arr))
+    return np.stack(frames)
+
+
+def _load_episode_video_frames(
+    dataset: LeRobotDataset,
+    key: str,
+    ep_idx: int,
+    frame_offsets: list[int],
+    is_depth: bool,
+) -> np.ndarray:
+    """Load sampled frames of a video feature for one episode as a (N, C, H, W) array."""
+    ep = dataset.meta.episodes[ep_idx]
+    video_path = dataset.root / dataset.meta.get_video_file_path(ep_idx, key)
+    from_timestamp = ep[f"videos/{key}/from_timestamp"]
+    timestamps = [from_timestamp + offset / dataset.meta.fps for offset in frame_offsets]
+
+    frames = decode_video_frames(
+        video_path,
+        timestamps,
+        dataset.tolerance_s,
+        backend=dataset._video_backend,
+        return_uint8=not is_depth,
+        is_depth=is_depth,
+    )
+    if is_depth:
+        # ``decode_video_frames`` returns raw 12-bit codec values; dequantize back to
+        # the recorded depth unit so stats match record-time stats (which are stored in
+        # ``info.depth_unit`` and only rescaled to the output unit on read).
+        info = dataset.meta.features[key].get("info") or {}
+        depth_encoder = DepthEncoderConfig.from_video_info(info)
+        frames = dequantize_depth(
+            frames,
+            depth_min=depth_encoder.depth_min,
+            depth_max=depth_encoder.depth_max,
+            shift=depth_encoder.shift,
+            use_log=depth_encoder.use_log,
+            output_unit=info.get("depth_unit") or DEFAULT_DEPTH_UNIT,
+        )
+    return np.stack([auto_downsample_height_width(frame) for frame in frames.numpy()])
+
+
+def _compute_visual_episode_stats(
+    dataset: LeRobotDataset,
+    ep_idx: int,
+    visual_keys: list[str],
+) -> dict:
+    """Compute per-episode statistics for image/video features by sampling frames.
+
+    Mirrors the image/video branch of :func:`compute_episode_stats`: per-channel stats
+    are computed on downsampled sampled frames, then RGB stats are rescaled to [0, 1]
+    (depth maps keep their native units).
+    """
+    ep_length = dataset.meta.episodes[ep_idx]["length"]
+    frame_offsets = sample_indices(ep_length)
+
+    ep_stats = {}
+    for key in visual_keys:
+        is_depth = key in dataset.meta.depth_keys
+        if dataset.meta.features[key]["dtype"] == "video":
+            frames = _load_episode_video_frames(dataset, key, ep_idx, frame_offsets, is_depth)
+        else:
+            frames = _load_episode_image_frames(dataset, key, ep_idx, frame_offsets, is_depth)
+
+        stats = get_feature_stats(frames, axis=(0, 2, 3), keepdims=True)
+        normalization_factor = 1.0 if is_depth else 255.0
+        ep_stats[key] = {
+            k: v if k == "count" else np.squeeze(v / normalization_factor, axis=0)
+            for k, v in stats.items()
+        }
+
+    return ep_stats
+
+
+def compute_dataset_episode_stats(
+    dataset: LeRobotDataset,
+    episode_indices: list[int] | None = None,
+    skip_image_video: bool = True,
+    drop_keys: list[str] | None = None,
+) -> dict[int, dict]:
+    """Compute per-episode statistics for a subset of episodes.
+
+    This is the shardable unit of work behind :func:`recompute_stats`: distribute
+    ``episode_indices`` across workers (e.g. ``list(range(n))[rank::world_size]``),
+    then combine the results with :func:`aggregate_episode_stats`.
+
+    Args:
+        dataset: The LeRobotDataset to compute stats for.
+        episode_indices: Episodes to process. When ``None``, all episodes are processed.
+        skip_image_video: If True (default), only numeric features are computed. If False,
+            image/video stats are also computed by sampling and decoding frames.
+        drop_keys: Feature keys to exclude (e.g. ``action`` when it is computed separately
+            in relative-action space).
+
+    Returns:
+        A mapping of episode index to its per-episode stat dict. Keeping the episode index
+        (rather than a bare list) lets callers write the stats back to the correct episode
+        row, and survives sharding since shards can be merged by key.
+    """
+    features = dataset.meta.features
+    meta_keys = {"index", "episode_index", "task_index", "frame_index", "timestamp"}
+    drop = set(drop_keys or [])
+    features_to_compute = {
+        k: v
+        for k, v in features.items()
+        if v["dtype"] != "string"
+        and k not in meta_keys
+        and k not in drop
+        and (not skip_image_video or v["dtype"] not in ["image", "video"])
+    }
+    numeric_keys = [k for k, v in features_to_compute.items() if v["dtype"] not in ["image", "video"]]
+    visual_keys = [k for k, v in features_to_compute.items() if v["dtype"] in ["image", "video"]]
+
+    if dataset.meta.episodes is None:
+        dataset.meta.episodes = load_episodes(dataset.meta.root)
+
+    if episode_indices is None:
+        episode_indices = list(range(dataset.meta.total_episodes))
+
+    # Group requested episodes by their data parquet file so each file is read once.
+    file_to_episodes: dict[Path, list[int]] = {}
+    for ep_idx in episode_indices:
+        file_to_episodes.setdefault(dataset.meta.get_data_file_path(ep_idx), []).append(ep_idx)
+
+    all_episode_stats = {}
+    for src_path, eps in tqdm(sorted(file_to_episodes.items()), desc="Computing stats from data files"):
+        df = pd.read_parquet(dataset.root / src_path) if numeric_keys else None
+        for ep_idx in sorted(eps):
+            episode_data = {}
+            if numeric_keys:
+                ep_df = df[df["episode_index"] == ep_idx]
+                for key in numeric_keys:
+                    if key in ep_df.columns:
+                        values = ep_df[key].values
+                        episode_data[key] = (
+                            np.stack(values) if hasattr(values[0], "__len__") else np.array(values)
+                        )
+
+            ep_stats = compute_episode_stats(episode_data, features_to_compute)
+            if visual_keys:
+                ep_stats.update(_compute_visual_episode_stats(dataset, int(ep_idx), visual_keys))
+            all_episode_stats[int(ep_idx)] = ep_stats
+
+    return all_episode_stats
+
+
 def recompute_stats(
    dataset: LeRobotDataset,
    skip_image_video: bool = True,
@@ -1566,13 +1740,21 @@ def recompute_stats(
    relative_exclude_joints: list[str] | None = None,
    chunk_size: int = 50,
    num_workers: int = 0,
+    update_episode_stats: bool = False,
 ) -> LeRobotDataset:
    """Recompute stats.json from scratch by iterating all episodes.

    Args:
        dataset: The LeRobotDataset to recompute stats for.
        skip_image_video: If True (default), only recompute stats for numeric features
-            (action, state, etc.) and keep existing image/video stats unchanged.
+            (action, state, etc.) and keep existing image/video stats unchanged. If False,
+            image/video stats are also recomputed by sampling and decoding frames from each
+            episode (this reads the image/video files, unlike the numeric-only path).
+        update_episode_stats: If True, also rewrite the per-episode ``stats/*`` columns in the
+            episodes parquet files so they stay consistent with the aggregated ``stats.json``.
+            Defaults to False (only ``stats.json`` is rewritten). Requires a writable
+            ``dataset.root``. Note that relative-action stats are aggregate-only and are not
+            written per-episode.
        relative_action: If True, compute action stats in relative space by
            iterating all valid action chunks and subtracting the current state.
            This matches the normalization distribution the model sees during
@@ -1588,24 +1770,12 @@ def recompute_stats(
        The same dataset with updated stats.
    """
    features = dataset.meta.features
-    meta_keys = {"index", "episode_index", "task_index", "frame_index", "timestamp"}
-    numeric_features = {
-        k: v
-        for k, v in features.items()
-        if v["dtype"] not in ["image", "video", "string"] and k not in meta_keys
-    }
-
-    if skip_image_video:
-        features_to_compute = numeric_features
-    else:
-        features_to_compute = {
-            k: v for k, v in features.items() if v["dtype"] != "string" and k not in meta_keys
-        }

    # When relative_action is enabled, compute action stats via chunk-based sampling
    # (matching what the model sees during training) and skip action in the
    # per-episode pass below.
    relative_action_stats = None
+    drop_keys = None
    if relative_action and ACTION in features and OBS_STATE in features:
        if relative_exclude_joints is None:
            relative_exclude_joints = ["gripper"]
@@ -1616,56 +1786,105 @@ def recompute_stats(
            exclude_joints=relative_exclude_joints,
            num_workers=num_workers,
        )
-        features_to_compute.pop(ACTION, None)
+        drop_keys = [ACTION]

-    logging.info(f"Recomputing stats for features: {list(features_to_compute.keys())}")
+    all_episode_stats = compute_dataset_episode_stats(
+        dataset, skip_image_video=skip_image_video, drop_keys=drop_keys
+    )

-    data_dir = dataset.root / DATA_DIR
-    parquet_files = sorted(data_dir.glob("*/*.parquet"))
-    if not parquet_files:
-        raise ValueError(f"No parquet files found in {data_dir}")
-
-    all_episode_stats = []
-    # TODO: enable image and video stats re-computation
-    numeric_keys = [k for k, v in features_to_compute.items() if v["dtype"] not in ["image", "video"]]
-
-    for parquet_path in tqdm(parquet_files, desc="Computing stats from data files"):
-        df = pd.read_parquet(parquet_path)
-
-        for ep_idx in sorted(df["episode_index"].unique()):
-            ep_df = df[df["episode_index"] == ep_idx]
-            episode_data = {}
-            for key in numeric_keys:
-                if key in ep_df.columns:
-                    values = ep_df[key].values
-                    if hasattr(values[0], "__len__"):
-                        episode_data[key] = np.stack(values)
-                    else:
-                        episode_data[key] = np.array(values)
-
-            ep_stats = compute_episode_stats(episode_data, features_to_compute)
-            all_episode_stats.append(ep_stats)
-
-    if features_to_compute and not all_episode_stats:
+    new_stats = aggregate_episode_stats(
+        dataset,
+        all_episode_stats,
+        extra_stats={ACTION: relative_action_stats} if relative_action_stats else None,
+        update_episode_stats=update_episode_stats,
+    )
+    if new_stats is None:
        logging.warning("No episode stats computed")
-        return dataset
+    else:
+        logging.info("Stats recomputed successfully")
+    return dataset

-    new_stats = aggregate_stats(all_episode_stats) if all_episode_stats else {}

-    if relative_action_stats is not None:
-        new_stats[ACTION] = relative_action_stats
+def write_episode_stats(dataset: LeRobotDataset, episode_stats: dict[int, dict]) -> None:
+    """Overwrite the per-episode ``stats/*`` columns in the episodes parquet files in place.

-    # Merge: keep existing stats for features we didn't recompute
+    Only the features present in ``episode_stats[ep_idx]`` are rewritten; stats columns for
+    features that were not recomputed are left untouched. Every other episode column (tasks,
+    length, chunk/file indices, frame ranges, …) is preserved. ``dataset.root`` must be
+    writable (e.g. the reference copy created for read-only sources).
+    """
+    if not episode_stats:
+        return
+
+    meta = dataset.meta
+    if meta.episodes is None:
+        meta.episodes = load_episodes(meta.root)
+
+    # Group episodes by the parquet file that holds them so each file is rewritten once.
+    file_to_episodes: dict[tuple[int, int], list[int]] = {}
+    for ep_idx in episode_stats:
+        ep = meta.episodes[ep_idx]
+        key = (ep["meta/episodes/chunk_index"], ep["meta/episodes/file_index"])
+        file_to_episodes.setdefault(key, []).append(ep_idx)
+
+    for (chunk_idx, file_idx), eps in file_to_episodes.items():
+        path = meta.root / DEFAULT_EPISODES_PATH.format(chunk_index=chunk_idx, file_index=file_idx)
+        table = pq.read_table(path)
+        rows = table.to_pylist()
+        row_by_ep = {row["episode_index"]: row for row in rows}
+        for ep_idx in eps:
+            row = row_by_ep[ep_idx]
+            for feature, feature_stats in episode_stats[ep_idx].items():
+                for stat_name, value in feature_stats.items():
+                    col = f"stats/{feature}/{stat_name}"
+                    if col in row:
+                        row[col] = np.asarray(value).tolist()
+        # Reuse the source schema so the rewritten stats keep the exact on-disk types.
+        new_table = pa.Table.from_pylist(rows, schema=table.schema)
+        pq.write_table(new_table, path, compression="snappy", use_dictionary=True)
+
+
+def aggregate_episode_stats(
+    dataset: LeRobotDataset,
+    episode_stats: dict[int, dict],
+    extra_stats: dict | None = None,
+    update_episode_stats: bool = False,
+) -> dict | None:
+    """Aggregate per-episode stats, merge with existing stats, and write ``stats.json``.
+
+    Companion to :func:`compute_dataset_episode_stats` for the distributed workflow: pass the
+    merged ``{episode_index: stats}`` mapping of every worker's per-episode stats. ``extra_stats``
+    lets callers inject feature stats computed outside the per-episode pass (e.g. relative-action
+    stats).
+
+    Args:
+        dataset: The dataset whose ``meta/stats.json`` (and optionally episode stats) is updated.
+        episode_stats: Mapping of episode index to its per-episode stat dict.
+        extra_stats: Feature stats to inject into the aggregate (not written per-episode).
+        update_episode_stats: If True, also rewrite the per-episode ``stats/*`` columns in the
+            episodes parquet files via :func:`write_episode_stats`.
+
+    Returns the written stats dict, or ``None`` if there was nothing to aggregate.
+    """
+    if not episode_stats and not extra_stats:
+        return None
+
+    new_stats = aggregate_stats(list(episode_stats.values())) if episode_stats else {}
+    if extra_stats:
+        new_stats.update(extra_stats)
+
+    # Merge: keep existing stats for features we didn't recompute.
    if dataset.meta.stats:
        for key, value in dataset.meta.stats.items():
-            if key not in new_stats:
-                new_stats[key] = value
+            new_stats.setdefault(key, value)

    write_stats(new_stats, dataset.root)
    dataset.meta.stats = new_stats

-    logging.info("Stats recomputed successfully")
-    return dataset
+    if update_episode_stats:
+        write_episode_stats(dataset, episode_stats)
+
+    return new_stats


 def convert_image_to_video_dataset(
@@ -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
@@ -167,7 +167,9 @@ Show dataset information without feature details:
        --operation.type info \
        --operation.show_features false

-Recompute dataset statistics (saves to lerobot/pusht_recomputed_stats by default):
+Recompute dataset statistics (saves to lerobot/pusht_recomputed_stats by default). The source
+dataset is never modified: large files are symlinked and only meta/ is copied, so this also works
+on read-only source datasets:
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
        --operation.type recompute_stats
@@ -178,6 +180,19 @@ Recompute stats and save to a specific new repo_id:
        --new_repo_id lerobot/pusht_new_stats \
        --operation.type recompute_stats

+Recompute stats including image/video features (samples and decodes frames from each episode):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --operation.type recompute_stats \
+        --operation.skip_image_video false
+
+Recompute stats and also rewrite the per-episode stats in the episodes parquet (keeps
+meta/stats.json and the per-episode stats consistent):
+    lerobot-edit-dataset \
+        --repo_id lerobot/pusht \
+        --operation.type recompute_stats \
+        --operation.update_episode_stats true
+
 Recompute stats in-place (overwrites original dataset stats):
    lerobot-edit-dataset \
        --repo_id lerobot/pusht \
@@ -325,6 +340,7 @@ class RecomputeStatsConfig(OperationConfig):
    relative_exclude_joints: list[str] | None = None
    chunk_size: int = 50
    num_workers: int = 0
+    update_episode_stats: bool = False
    overwrite: bool = False


@@ -377,6 +393,30 @@ def _resolve_io_paths(
    return output_repo_id, input_path, output_path


+def _reference_copy_dataset(input_root: Path, output_root: Path) -> None:
+    """Create a lightweight copy of a dataset that never modifies the source.
+
+    The directory tree is recreated with real directories, and every file is
+    symlinked to its source counterpart so no data is duplicated and the source is
+    only ever read. Files under ``meta/`` are instead copied as real, writable files
+    so that stats/info can be rewritten without touching the original. Symlinking
+    individual files (rather than whole directories) keeps ``push_to_hub`` working,
+    since ``Path.glob`` follows file symlinks but does not descend into symlinked
+    directories. This makes the operation safe on read-only source datasets.
+    """
+    for src in input_root.rglob("*"):
+        rel = src.relative_to(input_root)
+        dst = output_root / rel
+        if src.is_dir():
+            dst.mkdir(parents=True, exist_ok=True)
+        elif rel.parts[0] == "meta":
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            shutil.copyfile(src, dst)  # copyfile ignores source perms, so dst is writable
+        else:
+            dst.parent.mkdir(parents=True, exist_ok=True)
+            dst.symlink_to(src.resolve())
+
+
 def get_output_path(
    repo_id: str,
    new_repo_id: str | None,
@@ -674,14 +714,18 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
        )
        dataset = LeRobotDataset(cfg.repo_id, root=input_root)
    else:
-        logging.info(f"Copying dataset from {input_root} to {output_root}")
+        logging.info(f"Referencing dataset from {input_root} into {output_root} (source is left untouched)")
        if output_root.exists():
            backup_path = output_root.with_name(output_root.name + "_old")
            logging.warning(f"Output directory {output_root} already exists. Moving to {backup_path}")
            if backup_path.exists():
                shutil.rmtree(backup_path)
            shutil.move(output_root, backup_path)
-        shutil.copytree(input_root, output_root)
+        # recompute_stats only reads data/ and rewrites files under meta/ (stats.json, and
+        # the episodes parquet when update_episode_stats is set), so symlink the large
+        # immutable files and copy only meta/. This avoids duplicating the dataset and works
+        # even when the source dataset is read-only.
+        _reference_copy_dataset(input_root, output_root)
        dataset = LeRobotDataset(output_repo_id, root=output_root)

    logging.info(f"Recomputing stats for {cfg.repo_id}")
@@ -698,6 +742,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
        relative_exclude_joints=cfg.operation.relative_exclude_joints,
        chunk_size=cfg.operation.chunk_size,
        num_workers=cfg.operation.num_workers,
+        update_episode_stats=cfg.operation.update_episode_stats,
    )

    logging.info(f"Stats written to {dataset.root}")
@@ -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" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/c4/1f/fa18009dea8469069cca78a4e877a008ab78f08b064bfc9ab891579077ff/uvicorn-0.49.0.tar.gz", hash = "sha256:ebf4271aa580d9de97f93192d4595176df6e91f9aae919ca73e4fc07df1e66a3", size = 91284, upload-time = "2026-06-03T22:01:30.448Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/2e/41/06cce5dbb9f77591512957710ac709e60b12e6216a2f2d0d607fd49706e8/uvicorn-0.50.0.tar.gz", hash = "sha256:0c92e1bc2259cb7faa4fcef774a5966588f2e88542744550b66799fba10b76f1", size = 93257, upload-time = "2026-07-04T05:03:26.33Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/88/fa/e1388bbcf24ef3274f45c0c1c7b501fd14971037c1b6ee23610553307497/uvicorn-0.49.0-py3-none-any.whl", hash = "sha256:ba3d14c3ee7e41c6c654c46c9eb489d33213cdd30aa1696eab1374337c13f68f", size = 71376, upload-time = "2026-06-03T22:01:29.037Z" },
+    { url = "https://files.pythonhosted.org/packages/a0/3a/eb70620ca2bf8213603d5c731460687c49fee38b0072f0b4a637781f0a53/uvicorn-0.50.0-py3-none-any.whl", hash = "sha256:05f0eb19edf38208f79f43df8a63081b48df31b0cd1e5997be957a4dc97d1b19", size = 72716, upload-time = "2026-07-04T05:03:24.848Z" },
 ]

 [package.optional-dependencies]
Author	SHA1	Message	Date
CarolinePascal	baee9236dd	perf(examples): speed up slurm stats pre-submission on the login node Resolve the source root via metadata only instead of instantiating a full LeRobotDataset (which memory-maps the entire frame index just to read a path), and download videos only when the run actually recomputes image/video stats (derived from --skip-image-video). This avoids the multi-TB video download for numeric-only runs.	2026-07-11 00:22:10 +02:00
CarolinePascal	9372f52fff	feat(datasets): optionally rewrite per-episode stats when recomputing Add an opt-in update_episode_stats flag so recomputing stats can also rewrite the per-episode stats/* columns in the episodes parquet, keeping them consistent with meta/stats.json. compute_dataset_episode_stats now returns a {episode_index: stats} mapping so stats can be written back to the right episode and shards merge by key. Wired into recompute_stats, the lerobot-edit-dataset CLI, and the SLURM example (per-episode shards + --update-episode-stats).	2026-07-10 23:35:08 +02:00
CarolinePascal	a343dcc90d	fix(examples): make slurm stats pipeline steps self-contained The pipeline steps are pickled and run on workers where this script's module globals are unavailable, so referencing the module-level _load_dataset helper raised NameError. Inline dataset loading and shard paths into each run() with local imports, and let --venv-path and --env-command coexist.	2026-07-10 21:37:34 +02:00
CarolinePascal	fbe9c11b60	refactor(examples): replace hf-mount with HF_LEROBOT_HOME + venv/env-command in slurm stats script Drops the per-worker hf-mount machinery in favor of a shared HF_LEROBOT_HOME cache plus --venv-path and --env-command hooks, which is simpler and avoids node-local mount setup. Removes the now-unused os import.	2026-07-10 18:36:13 +02:00
CarolinePascal	e1e9934a78	feat(examples): add per-worker mount, QoS, and chained aggregate to slurm stats script Adds HPC cluster support to the SLURM stats recomputation example: a --qos passthrough, per-worker hf-mount of the read-only source via datatrove's env_command hook, and --chain-aggregate to submit aggregate with an afterok dependency on compute. Also switches to datatrove's native mem_per_cpu_gb field.	2026-07-10 16:56:16 +02:00
CarolinePascal	7035ecf9b2	fix(datasets): dequantize depth video frames when recomputing stats Depth video stats were computed on raw 12-bit codec values, leaving them in codec space instead of the recorded depth unit. Dequantize decoded frames via the feature's depth encoder config (matching DatasetReader) so recomputed stats match record-time stats. Also fix the SLURM example: the --skip-image-video flag was inverted (0 skipped visual stats), and add a --video-backend option so pyav can be used when torchcodec fails to load locally.	2026-07-10 16:25:40 +02:00
CarolinePascal	7bee7fb9e3	feat(datasets): distribute stats recomputation across SLURM workers Expose the shardable unit of work behind recompute_stats: compute_dataset_episode_stats computes per-episode stats for an episode subset, and aggregate_episode_stats merges the concatenated shards (count-weighted) and writes stats.json. recompute_stats now composes these, so single-process behavior is unchanged. Add examples/dataset/slurm_recompute_stats.py, a datatrove compute/aggregate driver that shards episodes across workers and is read-only safe (reference-copies the source when --new-root is given). Most useful for the expensive image/video stats path.	2026-07-10 14:47:18 +02:00
CarolinePascal	bf4c9174a8	feat(datasets): make recompute_stats read-only safe and support image/video stats Recompute stats without modifying the source dataset by symlinking the large immutable files (data/, videos/, images/) and copying only meta/ as writable files. This avoids duplicating the dataset and works on read-only sources (e.g. a mounted HF repo that isn't yours). Symlinking individual files keeps push_to_hub working. Also implement the previously-unfinished image/video stats recomputation: when skip_image_video=False, per-episode image/video stats are recomputed by sampling and decoding frames, mirroring compute_episode_stats.	2026-07-10 14:35:22 +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