feat(unitree_g1): use captured neutral SONIC token instead of zeros

The all-zero token is off the encoder's learned FSQ manifold and decodes to a
slightly goofy stance. Replace it with a NEUTRAL_TOKEN captured from the encoder's
own idle output in sim (stored as integer FSQ codes, rescaled by the encoder's
1/16 quantization step to an exact on-grid token). token_mode now seeds this
neutral, and the onboard sender starts observation.state from it so the first
inference sees the token the decoder is actually holding.

pyproject.toml

@@ -374,7 +374,11 @@ torch = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" }]
 torchvision = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux'" }]
 
 [tool.setuptools.package-data]
-lerobot = ["envs/*.json", "annotations/steerable_pipeline/prompts/*.txt"]
+lerobot = [
+    "envs/*.json",
+    "annotations/steerable_pipeline/prompts/*.txt",
+    "teleoperators/pico_headset/assets/*.npz",
+]
 
 [tool.setuptools.packages.find]
 where = ["src"]

src/lerobot/robots/unitree_g1/README.md

@@ -0,0 +1,89 @@
+# Unitree G1 — SONIC encoder/decoder whole-body control
+
+This package runs NVIDIA's **SONIC** encoder/decoder on the Unitree G1, in MuJoCo
+simulation or on real hardware, driven by a dense **34-D whole-body command** (the
+OpenHLM / pi0.5 action layout). It is a pure-Python/ONNX reimplementation of the
+reference-tracking half of the SONIC deploy stack (no `gear_sonic`/torch dependency, and
+no motion planner): the encoder compresses a reference motion window into a latent token
+and the decoder maps that token + proprioception history into 50 Hz joint-position
+targets for the robot's PD controller.
+
+## Controllers
+
+Selected with `--robot.controller=<ClassName>`:
+
+| Controller                     | Purpose                                                      |
+| ------------------------------ | ------------------------------------------------------------ |
+| `SonicWholeBodyController`     | SONIC encoder/decoder driven by a 34-D OpenHLM/pi0.5 command |
+| `GrootLocomotionController`    | GR00T locomotion policy                                      |
+| `HolosomaLocomotionController` | Holosoma locomotion policy                                   |
+
+The rest of this document covers the SONIC whole-body path.
+
+Each tick the `SonicWholeBodyController` takes a 34-D command (`wb.0.pos … wb.33.pos`) in the OpenHLM
+layout:
+
+```
+[L-arm(7), L-grip(1), R-arm(7), R-grip(1), L-leg(6), R-leg(6), waist(3),
+ root roll/pitch + yaw-rate(3)]
+```
+
+The 29 joint targets become the SONIC encode-mode-0 reference (accumulated into a rolling
+50-frame trajectory with finite-difference velocities so the encoder's lookahead sees a
+real motion sequence), the root roll/pitch set the anchor orientation, and the two grip
+scalars can drive the Dex3 hands (see below). On startup the controller **interpolates**
+from the robot's measured pose into the policy's commanded target over ~3 s (no snap).
+
+## Requirements
+
+- `onnxruntime` (CPU) **or** `onnxruntime-gpu` (recommended). Verify with:
+  ```bash
+  python -c "import onnxruntime as ort; print(ort.get_available_providers())"
+  ```
+- `mujoco` for simulation (`is_simulation=True`).
+- The SONIC encoder/decoder ONNX models download automatically from the
+  `nvidia/GEAR-SONIC` Hub repo.
+
+## Running a rollout
+
+Drive the G1 with a 34-D VLA policy (OpenHLM / pi0.5) via `lerobot-rollout`:
+
+```bash
+lerobot-rollout \
+  --strategy.type=base \
+  --policy.path=<pi05_openhlm_dir> \
+  --robot.type=unitree_g1 \
+  --robot.controller=SonicWholeBodyController \
+  --robot.is_simulation=true \
+  --robot.publish_hands=true \
+  --task="<language instruction>" \
+  --duration=45 --device=cuda
+```
+
+### Cameras
+
+Image-conditioned policies need camera frames. Two options are available without live
+cameras:
+
+- **Black frames**: `--robot.empty_cameras='[base, left_wrist, right_wrist]'`.
+- **Replay a recorded episode** as the camera feed:
+  ```bash
+  --robot.replay_camera_parquet=<episode.parquet> \
+  --robot.replay_camera_map='{base: head_image_left, left_wrist: left_wrist_image, right_wrist: right_wrist_image}'
+  ```
+
+### Hands (Dex3)
+
+`--robot.publish_hands=true` publishes `rt/dex3/{left,right}/cmd` from the two grip
+scalars (`wb.7.pos` left, `wb.15.pos` right). The scalar is interpolated between
+`hand_open_grip_value` (default 1.0 = open) and `hand_closed_grip_value` (default 0.0 =
+closed) and scaled onto `hand_closed_pose` (7 joints:
+`thumb_0, thumb_1, thumb_2, middle_0, middle_1, index_0, index_1`). Flip the signs in
+`hand_closed_pose` if the fingers curl the wrong way, or raise `hand_kp` for a firmer
+grip.
+
+## Observation state
+
+When the whole-body controller is active the robot exposes a 34-D proprio state
+(`wb_state.0.pos … wb_state.33.pos`) in the same OpenHLM layout as the action, which the
+rollout aggregates into `observation.state` for the policy.

src/lerobot/robots/unitree_g1/config_unitree_g1.py

@@ -62,12 +62,76 @@ class UnitreeG1Config(RobotConfig):
     # Socket config for ZMQ bridge
     robot_ip: str = "192.168.123.164"  # default G1 IP
 
+    # Run the locomotion / whole-body controller ONBOARD the robot (policy on the G1
+    # itself, against local DDS at full rate) instead of on the laptop over the ZMQ
+    # socket bridge. In this mode the robot object uses the real Unitree SDK channels
+    # and expects high-level actions (arm targets + joystick axes, or 64-D SONIC
+    # tokens) fed via send_action -- e.g. by run_g1_onboard.py, which receives them
+    # from the laptop over ZMQ. Mutually exclusive with is_simulation.
+    onboard: bool = False
+    # DDS network interface for onboard mode (None = SDK default, matching
+    # run_g1_server.py's ChannelFactoryInitialize(0)).
+    dds_interface: str | None = None
+    # Onboard sub-flags. On a real G1 both are True: the built-in motion services
+    # must be released before we can write lowcmd, and locomotion axes are read from
+    # the physical wireless remote. Against a DDS sim neither applies (no
+    # MotionSwitcher, no physical remote), so set both False so the controller takes
+    # its locomotion axes purely from send_action (ZMQ) input.
+    release_motion_control: bool = True
+    physical_remote: bool = True
+
     # Cameras (ZMQ-based remote cameras)
     cameras: dict[str, CameraConfig] = field(default_factory=dict)
 
+    # Synthetic zero-image cameras exposed as ``observation.images.{name}`` (H×W×3
+    # black frames). Lets image-conditioned policies (e.g. pi0.5 / OpenHLM) run in
+    # sim before real cameras are wired. Empty = disabled.
+    empty_cameras: list[str] = field(default_factory=list)
+    empty_camera_hw: tuple[int, int] = (224, 224)
+
+    # Publish Dex3 hand commands (``rt/dex3/{left,right}/cmd``) driven by the OpenHLM
+    # gripper scalars (``wb.7.pos`` left, ``wb.15.pos`` right). Lets the 43-DoF sim
+    # (or a real Dex3-equipped G1) show grasping. The scalar in [0, 1] is remapped to
+    # a curl amount (``hand_open_grip_value`` -> open) and scaled onto
+    # ``hand_closed_pose`` (7 joints: thumb_0/1/2, middle_0/1, index_0/1). Flip signs
+    # in ``hand_closed_pose`` if fingers curl the wrong way.
+    publish_hands: bool = False
+    # When False, connect() does not start the background controller thread, so a
+    # caller can drive the controller synchronously (one decode per fed action),
+    # reproducing the deploy's single 50Hz control clock for faithful replay.
+    run_controller_thread: bool = True
+    hand_open_grip_value: float = 1.0
+    hand_closed_grip_value: float = 0.0
+    hand_closed_pose: list[float] = field(default_factory=lambda: [1.0, 0.9, 0.9, 1.3, 1.3, 1.3, 1.3])
+    hand_kp: float = 1.5
+    hand_kd: float = 0.1
+
+    # Replay recorded camera frames from a LeRobot parquet episode as the camera
+    # feed (e.g. OpenHLM-data episode). Maps a robot camera name to a parquet image
+    # column; frames advance one per observation and loop. Lets a VLA see the real
+    # task video in sim without live cameras. Empty map = disabled.
+    replay_camera_parquet: str | None = None
+    replay_camera_map: dict[str, str] = field(default_factory=dict)
+    replay_camera_loop: bool = True
+
+    # Token-output VLA interface for the SONIC decoder. When True (and the controller
+    # is ``SonicWholeBodyController``), the robot advertises a 64-D latent-token action
+    # space (``motion_token.{i}.pos``) instead of the 34-D whole-body command, and
+    # exposes the last commanded token as a 64-D ``observation.state``
+    # (``motion_token_state.{i}.pos``). This lets ``lerobot-rollout`` drive a policy
+    # that was trained with 64-D SONIC motion tokens as both state and action
+    # (e.g. nepyope/sonic_walk): the decoder consumes the token directly, encoder
+    # bypassed. Ignored unless a SONIC whole-body controller is active.
+    sonic_token_action: bool = False
+
     # Compensates for gravity on the unitree's arms using the arm ik solver
     gravity_compensation: bool = False
 
-    # Lower-body controller class name, e.g. "GrootLocomotionController" or
+    # Locomotion controller class name, e.g. "GrootLocomotionController",
-    # "HolosomaLocomotionController". None disables it.
+    # "HolosomaLocomotionController", or "SonicWholeBodyController". None disables it.
     controller: str | None = None
+
+    # On disconnect (e.g. Ctrl-C), seconds to hold the current pose while ramping joint
+    # stiffness (kp) to zero — a soft, damped settle instead of an instant limp /
+    # free-fall. 0 disables it (immediate zero-torque). Real robot only.
+    graceful_stop_s: float = 1.5

src/lerobot/robots/unitree_g1/controllers/__init__.py

@@ -0,0 +1,28 @@
+#!/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.
+
+"""Unitree G1 locomotion controllers (Groot, Holosoma, SONIC)."""
+
+from .gr00t_locomotion import GrootLocomotionController
+from .holosoma_locomotion import HolosomaLocomotionController
+from .sonic_whole_body import SonicRuntime, SonicWholeBodyController
+
+__all__ = [
+    "GrootLocomotionController",
+    "HolosomaLocomotionController",
+    "SonicRuntime",
+    "SonicWholeBodyController",
+]

src/lerobot/robots/unitree_g1/controllers/gr00t_locomotion.py

@@ -14,20 +14,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import logging
 from collections import deque
+from typing import TYPE_CHECKING
 
 import numpy as np
-import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from .g1_utils import (
+from lerobot.utils.import_utils import _onnxruntime_available, require_package
+
+from ..g1_utils import (
     REMOTE_AXES,
     REMOTE_BUTTONS,
     G1_29_JointIndex,
     get_gravity_orientation,
 )
 
+if TYPE_CHECKING or _onnxruntime_available:
+    import onnxruntime as ort
+else:
+    ort = None
+
 logger = logging.getLogger(__name__)
 
 
@@ -68,9 +77,15 @@ def load_groot_policies(
         filename="GR00T-WholeBodyControl-Walk.onnx",
     )
 
-    # Load ONNX policies
+    # Load ONNX policies with a capped thread pool. GR00T runs at 50 Hz in a
-    policy_balance = ort.InferenceSession(balance_path)
+    # background thread alongside the (torch) upper-body policy, IK and sim; letting
-    policy_walk = ort.InferenceSession(walk_path)
+    # ORT grab every core starves those and makes the whole rollout stutter. These
+    # are small MLPs, so 1 thread is both enough and lowest-latency.
+    from .sonic_pipeline import make_ort_session_options
+
+    so = make_ort_session_options(intra_op_num_threads=1, inter_op_num_threads=1)
+    policy_balance = ort.InferenceSession(balance_path, sess_options=so)
+    policy_walk = ort.InferenceSession(walk_path, sess_options=so)
 
     logger.info("GR00T policies loaded successfully")
 
@@ -83,6 +98,7 @@ class GrootLocomotionController:
     control_dt = CONTROL_DT  # Expose for unitree_g1.py
 
     def __init__(self):
+        require_package("onnxruntime", extra="unitree_g1")
         # Load policies
         self.policy_balance, self.policy_walk = load_groot_policies()
 
@@ -196,6 +212,16 @@ class GrootLocomotionController:
         # Transform action back to target joint positions
         target_dof_pos_15 = GROOT_DEFAULT_ANGLES[:15] + self.groot_action * ACTION_SCALE
 
+        # Waist override: an external upper-body IK can command the 3 waist joints
+        # (indices 12/13/14) via ``kWaist{Yaw,Roll,Pitch}.q`` in the action dict. When
+        # present, we substitute the balance policy's waist target so the torso tracks
+        # the IK while the policy keeps only the legs balanced. Single-publisher stays
+        # intact (this thread still owns joints 0-14).
+        for idx in (G1_29_JointIndex.kWaistYaw, G1_29_JointIndex.kWaistRoll, G1_29_JointIndex.kWaistPitch):
+            key = f"{idx.name}.q"
+            if key in action and action[key] is not None:
+                target_dof_pos_15[idx.value] = float(action[key])
+
         # Build action dict
         action_dict = {}
         for i in range(15):

src/lerobot/robots/unitree_g1/controllers/holosoma_locomotion.py

@@ -14,21 +14,34 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 import json
 import logging
+from typing import TYPE_CHECKING
 
 import numpy as np
-import onnx
-import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from .g1_utils import (
+from lerobot.utils.import_utils import _onnx_available, _onnxruntime_available, require_package
+
+from ..g1_utils import (
     REMOTE_AXES,
     G1_29_JointArmIndex,
     G1_29_JointIndex,
     get_gravity_orientation,
 )
 
+if TYPE_CHECKING or _onnxruntime_available:
+    import onnxruntime as ort
+else:
+    ort = None
+
+if TYPE_CHECKING or _onnx_available:
+    import onnx
+else:
+    onnx = None
+
 logger = logging.getLogger(__name__)
 
 DEFAULT_ANGLES = np.zeros(29, dtype=np.float32)
@@ -101,6 +114,8 @@ class HolosomaLocomotionController:
     control_dt = CONTROL_DT  # Expose for unitree_g1.py
 
     def __init__(self):
+        require_package("onnxruntime", extra="unitree_g1")
+        require_package("onnx", extra="unitree_g1")
         # Load policy and gains
         self.policy, self.kp, self.kd = load_policy()
 

src/lerobot/robots/unitree_g1/controllers/sonic_pipeline.py

@@ -0,0 +1,670 @@
+#!/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.
+
+"""SONIC encoder/decoder pipeline for the Unitree G1 whole-body controller.
+
+Pure-Python/ONNX re-implementation of the reference-tracking half of NVIDIA's SONIC
+deploy stack (mirrors ``g1_deploy_onnx_ref.cpp``). Given a reference motion buffer
+(joint targets + body orientation per frame) it produces 50 Hz joint-position targets
+for the robot's PD controller. The upstream *motion planner* is intentionally absent:
+here the reference is supplied directly by the caller (e.g. a 34-D OpenHLM / pi0.5 VLA
+command per tick, in ``sonic_whole_body.py``).
+
+Two cooperating ONNX models:
+  * **encoder** – compresses the reference window into a 64-D latent ``token``
+    (refreshed every ``ENCODER_UPDATE_EVERY`` ticks).
+  * **decoder** – every tick, maps the token + recent proprioception history to a
+    residual action that is scaled and added to ``DEFAULT_ANGLES``.
+
+Index spaces: joints exist in two orderings — **IsaacLab** (policy/training order)
+and **MuJoCo** (deploy order). ``ISAACLAB_TO_MUJOCO`` / ``MUJOCO_TO_ISAACLAB`` convert
+between them. Quaternions are scalar-first ``(w, x, y, z)``.
+"""
+
+from __future__ import annotations
+
+import logging
+import threading
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from lerobot.utils.import_utils import _onnxruntime_available
+
+from ..g1_utils import (
+    ISAACLAB_TO_MUJOCO,
+    MUJOCO_TO_ISAACLAB,
+    G1_29_JointIndex,
+    get_gravity_orientation,
+)
+
+if TYPE_CHECKING or _onnxruntime_available:
+    import onnxruntime as ort
+else:
+    ort = None
+
+logger = logging.getLogger(__name__)
+
+# ── Constants ────────────────────────────────────────────────────────────────
+# Robot/motor physical constants and the joint-order permutation tables. All
+# 29-vectors are in IsaacLab joint order unless the name says ``_MUJOCO``.
+
+# Nominal standing pose (rad), 29 joints in IsaacLab order. Actions are residuals
+# added on top of this; also used as the planner/encoder standing reference.
+DEFAULT_ANGLES = np.array(
+    [
+        -0.312,
+        0.0,
+        0.0,
+        0.669,
+        -0.363,
+        0.0,
+        -0.312,
+        0.0,
+        0.0,
+        0.669,
+        -0.363,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.2,
+        0.2,
+        0.0,
+        0.6,
+        0.0,
+        0.0,
+        0.0,
+        0.2,
+        -0.2,
+        0.0,
+        0.6,
+        0.0,
+        0.0,
+        0.0,
+    ],
+    dtype=np.float32,
+)
+
+# Per-motor-type parameters used to derive action scaling and PD gains. Keys are
+# Unitree motor model names; ARMATURE = rotor inertia, EFFORT = torque limit (N·m).
+NATURAL_FREQ = 10.0 * 2.0 * np.pi  # target closed-loop stiffness bandwidth (rad/s)
+ARMATURE = {"5020": 0.003609725, "7520_14": 0.010177520, "7520_22": 0.025101925, "4010": 0.00425}
+EFFORT = {"5020": 25.0, "7520_14": 88.0, "7520_22": 139.0, "4010": 5.0}
+
+
+def _action_scale(k):
+    """Per-motor residual-action scale (maps policy output to joint-angle delta)."""
+    return 0.25 * EFFORT[k] / (ARMATURE[k] * NATURAL_FREQ**2)
+
+
+# Per-joint motor model (IsaacLab order): legs, waist, then arms. Single source of
+# truth for both ACTION_SCALE and compute_kp_kd().
+MOTOR_MODELS = (
+    ["7520_22", "7520_22", "7520_14", "7520_22", "5020", "5020"] * 2
+    + ["7520_14", "5020", "5020"]
+    + ["5020", "5020", "5020", "5020", "5020", "4010", "4010"] * 2
+)
+ACTION_SCALE = np.array([_action_scale(k) for k in MOTOR_MODELS], dtype=np.float32)  # (29,) IsaacLab order
+
+CONTROL_DT = 0.02  # 50 Hz control period (s)
+DEFAULT_HEIGHT = 0.788740  # nominal pelvis height (m)
+TOKEN_DIM = 64  # encoder latent size
+ENCODER_UPDATE_EVERY = 5  # refresh the encoder token every N ticks (decoder runs every tick)
+DEBUG_PRINT_EVERY = 100  # ticks between debug prints
+
+
+def _to_mujoco(a):
+    """Apply the ``MUJOCO_TO_ISAACLAB`` gather to a 29-vector (deploy-order reorder).
+
+    NOTE: this returns ``a[MUJOCO_TO_ISAACLAB]``. The ``_mj`` suffixes and the exact
+    permutation direction throughout this module are a fixed convention validated
+    against the deployed SONIC ONNX policy (the encoder/decoder consume vectors in
+    this order). Do not "correct" the table or rename toward the opposite direction
+    without re-validating on hardware — the labels are historical, the ordering is
+    load-bearing.
+    """
+    return a[MUJOCO_TO_ISAACLAB]
+
+
+DEFAULT_ANGLES_MUJOCO = _to_mujoco(DEFAULT_ANGLES)
+ENCODER_STANDING_REF = DEFAULT_ANGLES.copy()
+
+# Joint-index subsets (IsaacLab order) used to slice encoder observations.
+LOWER_BODY_IL = np.array([0, 3, 6, 9, 13, 17, 1, 4, 7, 10, 14, 18], dtype=np.int32)  # 12 leg joints
+WRIST_IL = np.array([23, 24, 25, 26, 27, 28], dtype=np.int32)  # 6 wrist joints
+VR_TARGET_DEF = np.zeros(9, dtype=np.float32)  # 3-point VR position targets (mode 1)
+VR_ORN_DEF = np.array([1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], dtype=np.float32)  # VR orn targets (mode 1)
+SMPL_DEF = np.zeros(720, dtype=np.float32)  # SMPL whole-body window default (mode 2)
+
+# ── PD gains ─────────────────────────────────────────────────────────────────
+
+
+def compute_kp_kd():
+    """Derive per-joint PD gains (kp, kd) from motor armature and target bandwidth.
+
+    Ankle and waist joints get a x2 factor for extra stiffness. Returns two
+    (29,) float32 arrays in IsaacLab joint order.
+    """
+
+    def s(k):
+        return ARMATURE[k] * NATURAL_FREQ**2
+
+    def d(k):
+        return 2.0 * 2.0 * ARMATURE[k] * NATURAL_FREQ
+
+    _double = {4, 5, 10, 11, 13, 14}  # ankle + waist indices with factor 2
+    kp = np.array([2 * s(k) if i in _double else s(k) for i, k in enumerate(MOTOR_MODELS)], dtype=np.float32)
+    kd = np.array([2 * d(k) if i in _double else d(k) for i, k in enumerate(MOTOR_MODELS)], dtype=np.float32)
+    return kp, kd
+
+
+_kp_kd = compute_kp_kd  # backward-compatible alias
+
+
+# ── Quaternion helpers ────────────────────────────────────────────────────────
+# All quaternions are scalar-first (w, x, y, z). "heading" = yaw-only quaternion.
+
+
+def quat_conj(q):
+    """Quaternion conjugate (inverse for unit quaternions)."""
+    return np.array([q[0], -q[1], -q[2], -q[3]], dtype=np.float32)
+
+
+def quat_mul(q1, q2):
+    """Hamilton product ``q1 ⊗ q2``."""
+    w1, x1, y1, z1 = q1
+    w2, x2, y2, z2 = q2
+    return np.array(
+        [
+            w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2,
+            w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2,
+            w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2,
+            w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2,
+        ],
+        dtype=np.float32,
+    )
+
+
+def quat_to_6d(q):
+    """Quaternion → 6-D rotation representation (first two rotated basis rows)."""
+    w, x, y, z = q
+    return np.array(
+        [
+            1 - 2 * (y * y + z * z),
+            2 * (x * y - z * w),
+            2 * (x * y + z * w),
+            1 - 2 * (x * x + z * z),
+            2 * (x * z - y * w),
+            2 * (y * z + x * w),
+        ],
+        dtype=np.float32,
+    )
+
+
+def calc_heading(q):
+    """Extract the yaw (heading) angle in radians from a quaternion."""
+    w, x, y, z = q
+    return float(np.arctan2(2 * (x * y + w * z), 1 - 2 * (y * y + z * z)))
+
+
+def heading_quat(q, sign=1.0):
+    """Yaw-only quaternion for ``q``'s heading (``sign=-1`` gives its inverse)."""
+    a = sign * calc_heading(q) / 2.0
+    return np.array([np.cos(a), 0, 0, np.sin(a)], dtype=np.float64)
+
+
+def heading_quat_inv(q):
+    """Inverse yaw-only quaternion for ``q``'s heading."""
+    return heading_quat(q, -1.0)
+
+
+def quat_slerp(q0, q1, t):
+    """Spherical linear interpolation between two quaternions (scalar ``t``)."""
+    q0 = q0 / (np.linalg.norm(q0) + 1e-12)
+    q1 = q1 / (np.linalg.norm(q1) + 1e-12)
+    dot = float(np.dot(q0, q1))
+    if dot < 0:
+        q1, dot = -q1, -dot
+    dot = min(dot, 1.0)
+    if dot > 0.9995:
+        r = q0 + t * (q1 - q0)
+        return r / (np.linalg.norm(r) + 1e-12)
+    th = np.arccos(dot)
+    st = np.sin(th)
+    return (np.sin((1 - t) * th) / st) * q0 + (np.sin(t * th) / st) * q1
+
+
+def quat_slerp_batch(q0, q1, t):
+    """Vectorized slerp over arrays of quaternions with a per-row parameter ``t``."""
+    q0 = q0 / (np.linalg.norm(q0, axis=1, keepdims=True) + 1e-12)
+    q1 = q1 / (np.linalg.norm(q1, axis=1, keepdims=True) + 1e-12)
+    dot = np.sum(q0 * q1, axis=1)
+    neg = dot < 0
+    q1 = q1.copy()
+    q1[neg] = -q1[neg]
+    dot[neg] = -dot[neg]
+    dot = np.clip(dot, -1, 1)
+    lin = dot > 0.9995
+    th = np.arccos(dot)
+    st = np.where(np.sin(th) == 0, 1, np.sin(th))
+    c0 = np.sin((1 - t) * th) / st
+    c1 = np.sin(t * th) / st
+    c0[lin] = 1 - t[lin]
+    c1[lin] = t[lin]
+    r = c0[:, None] * q0 + c1[:, None] * q1
+    return r / (np.linalg.norm(r, axis=1, keepdims=True) + 1e-12)
+
+
+def ort_providers(force_cpu: bool = False) -> list[str]:
+    """Prefer CUDA for enc/dec/planner (matches deploy when onnxruntime-gpu is installed)."""
+    avail = ort.get_available_providers()
+    if not force_cpu and "CUDAExecutionProvider" in avail:
+        return ["CUDAExecutionProvider", "CPUExecutionProvider"]
+    return ["CPUExecutionProvider"]
+
+
+def make_ort_session_options(intra_op_num_threads: int | None = None,
+                             inter_op_num_threads: int | None = None):
+    """Build ONNX Runtime SessionOptions (quiet logging).
+
+    Pass thread counts to cap ORT's CPU pool. These tiny MLP policies are latency-
+    bound, not throughput-bound, so letting ORT grab every core just starves the
+    real-time control loop / torch policy / IK solver and causes stutter. 1 intra +
+    1 inter thread is plenty and lowest-latency for a per-step MLP inference.
+    """
+    so = ort.SessionOptions()
+    so.log_severity_level = 3
+    if intra_op_num_threads is not None:
+        so.intra_op_num_threads = intra_op_num_threads
+    if inter_op_num_threads is not None:
+        so.inter_op_num_threads = inter_op_num_threads
+    return so
+
+
+# ── Encoder / Decoder ─────────────────────────────────────────────────────────
+
+
+class StandingEncoderDecoder:
+    """Runs the encoder + decoder ONNX models and owns the proprioception history.
+
+    Each tick it appends the latest robot state to 10-frame history buffers, builds
+    the encoder observation (1762-D, layout depends on ``encode_mode``) to refresh
+    the 64-D ``token``, then builds the decoder observation (994-D) and maps
+    ``token + history`` to a residual action added onto ``DEFAULT_ANGLES``.
+
+    ``PlannerController`` subclasses this to source the reference from a live,
+    planner-generated motion buffer instead of a fixed standing pose.
+    """
+
+    def __init__(self, encoder, decoder):
+        self.encoder, self.decoder = encoder, decoder
+        self.encoder_input = encoder.get_inputs()[0].name
+        self.decoder_input = decoder.get_inputs()[0].name
+        enc_dim = int(encoder.get_inputs()[0].shape[1])
+        dec_dim = int(decoder.get_inputs()[0].shape[1])
+        if enc_dim != 1762 or dec_dim != 994:
+            raise RuntimeError(f"Unexpected dims encoder={enc_dim}, decoder={dec_dim}")
+        self.token = np.zeros(TOKEN_DIM, np.float32)
+        self.last_action_mj = np.zeros(29, np.float32)
+        self.h_q_mj = [np.zeros(29, np.float32)] * 10
+        self.h_dq_mj = [np.zeros(29, np.float32)] * 10
+        self.h_ang = [np.zeros(3, np.float32)] * 10
+        self.h_act_mj = [np.zeros(29, np.float32)] * 10
+        self.h_quat = [np.array([1, 0, 0, 0], np.float32)] * 10
+        self.init_base_quat = np.array([1, 0, 0, 0], np.float32)
+        self.init_ref_quat = np.array([1, 0, 0, 0], np.float32)
+        self._heading_init = False
+        self.encode_mode = 0
+        self.vr_3point_local_target = VR_TARGET_DEF.copy()
+        self.vr_3point_local_orn_target = VR_ORN_DEF.copy()
+        self.smpl_joints_10frame_step1 = SMPL_DEF.copy()
+        # Optional per-frame SMPL root orientation (wxyz) for the mode-2 anchor.
+        # When None, the anchor falls back to the planner reference body quat.
+        self.smpl_root_quat = None
+        self.set_zero_reference()
+
+    def reset(self):
+        """Clear the token, 10-frame proprioception history and heading init.
+
+        ``UnitreeG1.reset()`` relies on this so the first decoder outputs of a new
+        episode are not contaminated by the previous episode's state.
+        """
+        self.token = np.zeros(TOKEN_DIM, np.float32)
+        self.last_action_mj = np.zeros(29, np.float32)
+        self.h_q_mj = [np.zeros(29, np.float32)] * 10
+        self.h_dq_mj = [np.zeros(29, np.float32)] * 10
+        self.h_ang = [np.zeros(3, np.float32)] * 10
+        self.h_act_mj = [np.zeros(29, np.float32)] * 10
+        self.h_quat = [np.array([1, 0, 0, 0], np.float32)] * 10
+        self.init_base_quat = np.array([1, 0, 0, 0], np.float32)
+        self.init_ref_quat = np.array([1, 0, 0, 0], np.float32)
+        self._heading_init = False
+
+    def update_history(self, q, dq, ang, quat):
+        """Push the latest proprioception (pos/vel/gyro/orientation) into the 10-frame buffers."""
+        quat = quat / (np.linalg.norm(quat) + 1e-8)
+        q_mj = _to_mujoco(q)
+        dq_mj = _to_mujoco(dq)
+        self.h_q_mj = [q_mj - DEFAULT_ANGLES_MUJOCO] + self.h_q_mj[:-1]
+        self.h_dq_mj = [dq_mj] + self.h_dq_mj[:-1]
+        self.h_ang = [ang.copy()] + self.h_ang[:-1]
+        self.h_act_mj = [self.last_action_mj.copy()] + self.h_act_mj[:-1]
+        self.h_quat = [quat.copy()] + self.h_quat[:-1]
+        if not self._heading_init:
+            self.init_base_quat = quat.copy()
+            self._heading_init = True
+
+    def _heading_quat(self, q):
+        h = calc_heading(q) / 2.0
+        return np.array([np.cos(h), 0, 0, np.sin(h)], np.float32)
+
+    def _heading_quat_inv(self, q):
+        h = calc_heading(q) / 2.0
+        return np.array([np.cos(-h), 0, 0, np.sin(-h)], np.float32)
+
+    def _anchor_6d(self, base_quat, ref_quat=None):
+        """6-D orientation error between the robot base and the (heading-aligned) reference."""
+        if ref_quat is None:
+            ref_quat = self.init_ref_quat
+        delta = quat_mul(self._heading_quat(self.init_base_quat), self._heading_quat_inv(self.init_ref_quat))
+        new_ref = quat_mul(delta, ref_quat)
+        return quat_to_6d(quat_mul(quat_conj(base_quat), new_ref))
+
+    def set_zero_reference(self):
+        """Initialize the reference to a single standing frame (used before a plan exists)."""
+        self.motion_joint_positions = [ENCODER_STANDING_REF.copy()]
+        self.motion_joint_velocities = [np.zeros(29, np.float32)]
+        self.motion_body_quats = [np.array([1, 0, 0, 0], np.float32)]
+        self.motion_body_z = [DEFAULT_HEIGHT]
+        self.motion_timesteps = 1
+        self.freeze_ref_frame = 0
+        self.init_ref_quat = self.motion_body_quats[0].copy()
+
+    def build_encoder_obs(self):
+        """Assemble the 1762-D encoder input; slot layout depends on ``encode_mode``.
+
+        mode 0 = locomotion (ref joint pos + anchor), 1 = 3-point VR teleop
+        (lower-body ref + VR targets), 2 = SMPL whole-body window + anchor/wrist.
+        """
+        obs = np.zeros(1762, np.float32)
+        obs[0] = float(self.encode_mode)
+        rf = min(self.freeze_ref_frame, self.motion_timesteps - 1)
+        ref_pos, ref_quat = self.motion_joint_positions[rf], self.motion_body_quats[rf]
+        if self.encode_mode == 0:
+            for f in range(10):
+                obs[4 + 29 * f : 4 + 29 * (f + 1)] = ref_pos
+                obs[601 + 6 * f : 601 + 6 * (f + 1)] = self._anchor_6d(self.h_quat[0], ref_quat)
+        elif self.encode_mode == 1:
+            ref_lower = ref_pos[LOWER_BODY_IL]
+            for f in range(10):
+                obs[661 + 12 * f : 661 + 12 * (f + 1)] = ref_lower
+            obs[901:910] = self.vr_3point_local_target
+            obs[910:922] = self.vr_3point_local_orn_target
+            obs[595:601] = self._anchor_6d(self.h_quat[0], ref_quat)
+        elif self.encode_mode == 2:
+            # Prefer the SMPL clip/stream root orientation for the anchor; fall
+            # back to the planner reference body quat when no root is provided.
+            anchor_ref = self.smpl_root_quat if self.smpl_root_quat is not None else ref_quat
+            obs[922:1642] = self.smpl_joints_10frame_step1
+            for f in range(10):
+                obs[1642 + 6 * f : 1642 + 6 * (f + 1)] = self._anchor_6d(self.h_quat[0], anchor_ref)
+                obs[1702 + 6 * f : 1702 + 6 * (f + 1)] = ref_pos[WRIST_IL]
+        else:
+            raise RuntimeError(f"Unsupported encoder mode: {self.encode_mode}")
+        return obs
+
+    def build_decoder_obs(self):
+        """Assemble the 994-D decoder input: token + 10-frame proprioception history + gravity."""
+        obs = np.zeros(994, np.float32)
+        off = 0
+        obs[off : off + 64] = self.token
+        off += 64
+        for h, sz in [
+            (list(reversed(self.h_ang)), 3),
+            (list(reversed(self.h_q_mj)), 29),
+            (list(reversed(self.h_dq_mj)), 29),
+            (list(reversed(self.h_act_mj)), 29),
+        ]:
+            for f in range(10):
+                obs[off : off + sz] = h[f]
+                off += sz
+        for q in reversed(self.h_quat):
+            obs[off : off + 3] = get_gravity_orientation(q)
+            off += 3
+        assert off == 994, f"Decoder obs mismatch: {off}"
+        return obs
+
+    def run_encoder(self):
+        """Run the encoder ONNX model and return the fresh 64-D token."""
+        return (
+            self.encoder.run(None, {self.encoder_input: self.build_encoder_obs().reshape(1, -1)})[0]
+            .squeeze()
+            .astype(np.float32)
+        )
+
+    def step(self, robot_obs, update_encoder, debug=False):
+        """One control tick: read robot obs, (optionally) re-encode, decode → joint targets.
+
+        Args:
+            robot_obs: dict with ``<joint>.q``/``.dq`` and ``imu.*`` fields.
+            update_encoder: refresh the token this tick (else reuse the cached one).
+            debug: print action/delta norms.
+
+        Returns:
+            dict of ``<joint>.q`` target positions (rad) in IsaacLab joint order.
+        """
+        jnames = [m.name for m in G1_29_JointIndex]
+        q = np.array(
+            [
+                robot_obs.get(f"{n}.q", DEFAULT_ANGLES[m.value])
+                for m, n in zip(G1_29_JointIndex, jnames, strict=False)
+            ],
+            np.float32,
+        )
+        dq = np.array([robot_obs.get(f"{n}.dq", 0.0) for n in jnames], np.float32)
+        quat = np.array(
+            [
+                robot_obs.get("imu.quat.w", 1),
+                robot_obs.get("imu.quat.x", 0),
+                robot_obs.get("imu.quat.y", 0),
+                robot_obs.get("imu.quat.z", 0),
+            ],
+            np.float32,
+        )
+        ang = np.array([robot_obs.get(f"imu.gyro.{a}", 0) for a in "xyz"], np.float32)
+        self.update_history(q, dq, ang, quat)
+        if update_encoder:
+            self.token = self.run_encoder()
+        action_mj = (
+            self.decoder.run(None, {self.decoder_input: self.build_decoder_obs().reshape(1, -1)})[0]
+            .squeeze()
+            .astype(np.float32)
+        )
+        self.last_action_mj = action_mj.copy()
+        target = DEFAULT_ANGLES + action_mj[ISAACLAB_TO_MUJOCO] * ACTION_SCALE
+        if debug:
+            delta = target - q
+            logger.debug(
+                "token_norm=%.4f action_norm=%.4f delta_max=%.4f delta_rms=%.4f",
+                np.linalg.norm(self.token),
+                np.linalg.norm(action_mj),
+                np.max(np.abs(delta)),
+                np.sqrt(np.mean(delta**2)),
+            )
+        return {f"{m.name}.q": float(target[m.value]) for m in G1_29_JointIndex}
+
+
+class PlannerController(StandingEncoderDecoder):
+    """Encoder/decoder driven by a caller-supplied, rolling motion buffer.
+
+    Extends ``StandingEncoderDecoder`` so the reference comes from a motion buffer
+    (a lookahead window with per-frame velocities) instead of a single fixed pose,
+    and handles heading re-initialization on the first frame / after a reset.
+    ``motion_lock`` guards the buffer, which the whole-body controller rewrites each
+    tick from the incoming command. The class name is retained for continuity with
+    the SONIC reference; no motion planner is involved.
+    """
+
+    def __init__(self, encoder, decoder):
+        super().__init__(encoder, decoder)
+        self.ref_cursor = 0
+        self.motion_timesteps = 0
+        self.motion_joint_positions = np.zeros((1500, 29), np.float64)
+        self.motion_joint_velocities = np.zeros((1500, 29), np.float64)
+        self.motion_body_quats = np.zeros((1500, 4), np.float64)
+        self.motion_body_quats[:, 0] = 1.0
+        self.motion_body_pos = np.zeros((1500, 3), np.float64)
+        self.init_ref_quat = np.array([1, 0, 0, 0], np.float64)
+        self.heading_init_base_quat = np.array([1, 0, 0, 0], np.float64)
+        self.delta_heading = 0.0
+        self.reinit_heading = False
+        self.playing = self.first_motion = False
+        self.motion_lock = threading.Lock()
+
+    def reset(self):
+        """Full reset: clear enc/dec state (super) plus the motion buffer and heading.
+
+        Forces a heading re-init on the next ``step`` so the reference frame is
+        re-latched to the post-reset robot orientation.
+        """
+        super().reset()
+        with self.motion_lock:
+            self.ref_cursor = 0
+            self.motion_timesteps = 0
+            self.motion_joint_positions[:] = 0.0
+            self.motion_joint_velocities[:] = 0.0
+            self.motion_body_quats[:] = 0.0
+            self.motion_body_quats[:, 0] = 1.0
+            self.motion_body_pos[:] = 0.0
+        self.init_ref_quat = np.array([1, 0, 0, 0], np.float64)
+        self.heading_init_base_quat = np.array([1, 0, 0, 0], np.float64)
+        self.delta_heading = 0.0
+        self.first_motion = False
+        self.playing = False
+        self.reinit_heading = True
+
+    def _heading_apply_delta(self):
+        """Heading correction quaternion (init base-vs-ref heading + operator ``delta_heading``)."""
+        delta = quat_mul(
+            heading_quat(self.heading_init_base_quat).astype(np.float32),
+            heading_quat_inv(self.init_ref_quat).astype(np.float32),
+        )
+        if self.delta_heading:
+            h = self.delta_heading / 2.0
+            delta = quat_mul(np.array([np.cos(h), 0, 0, np.sin(h)], np.float32), delta)
+        return delta
+
+    def _anchor_6d(self, base_quat, ref_quat=None):
+        """6-D base-vs-reference orientation error, including the operator heading delta."""
+        if ref_quat is None:
+            ref_quat = self.init_ref_quat
+        new_ref = quat_mul(self._heading_apply_delta(), ref_quat.astype(np.float32))
+        return quat_to_6d(quat_mul(quat_conj(base_quat.astype(np.float32)), new_ref))
+
+    def build_encoder_obs(self):
+        """Encoder input sourced from the live motion buffer (mode 0/2), lock-protected."""
+        obs = np.zeros(1762, np.float32)
+        obs[0] = float(self.encode_mode)
+        with self.motion_lock:
+            if self.encode_mode == 2:
+                # SMPL whole-body imitation: the 720-dim SMPL window carries the
+                # target pose; the planner reference frame supplies anchor + wrist.
+                rf = min(self.ref_cursor, self.motion_timesteps - 1)
+                ref_pos = self.motion_joint_positions[rf].astype(np.float32)
+                ref_quat = self.motion_body_quats[rf].astype(np.float32)
+                # Prefer the SMPL clip/stream root orientation (if provided) so the
+                # anchor tracks the operator's/clip's heading; else planner ref.
+                if self.smpl_root_quat is not None:
+                    ref_quat = np.asarray(self.smpl_root_quat, np.float32)
+                anchor = self._anchor_6d(self.h_quat[0], ref_quat)
+                wrist = ref_pos[WRIST_IL]
+                obs[922:1642] = self.smpl_joints_10frame_step1
+                for f in range(10):
+                    obs[1642 + 6 * f : 1642 + 6 * (f + 1)] = anchor
+                    obs[1702 + 6 * f : 1702 + 6 * (f + 1)] = wrist
+                return obs
+            if self.encode_mode == 1:
+                # 3-point VR teleop: the upper body tracks the VR wrist/neck targets
+                # while the planner reference supplies the lower body + anchor. Lower
+                # body is per-frame (step 5) like mode 0; the VR targets are current.
+                rf = min(self.ref_cursor, self.motion_timesteps - 1)
+                obs[595:601] = self._anchor_6d(self.h_quat[0], self.motion_body_quats[rf].astype(np.float32))
+                for f in range(10):
+                    tf = min(
+                        self.ref_cursor + f * 5 if self.playing else self.ref_cursor,
+                        self.motion_timesteps - 1,
+                    )
+                    ref_lower = self.motion_joint_positions[tf].astype(np.float32)[LOWER_BODY_IL]
+                    obs[661 + 12 * f : 661 + 12 * (f + 1)] = ref_lower
+                obs[901:910] = self.vr_3point_local_target
+                obs[910:922] = self.vr_3point_local_orn_target
+                return obs
+            for f in range(10):
+                tf = min(
+                    self.ref_cursor + f * 5 if self.playing else self.ref_cursor, self.motion_timesteps - 1
+                )
+                obs[4 + 29 * f : 4 + 29 * (f + 1)] = self.motion_joint_positions[tf].astype(np.float32)
+                if self.playing:
+                    obs[294 + 29 * f : 294 + 29 * (f + 1)] = self.motion_joint_velocities[tf].astype(
+                        np.float32
+                    )
+                obs[601 + 6 * f : 601 + 6 * (f + 1)] = self._anchor_6d(
+                    self.h_quat[0], self.motion_body_quats[tf].astype(np.float32)
+                )
+        return obs
+
+    def step(self, robot_obs, update_encoder, debug=False):
+        """Re-init the heading reference on first frame / after a reset, then run the base step."""
+        if robot_obs and (self.first_motion or self.reinit_heading):
+            q = None
+            if "imu.quat.w" in robot_obs:
+                q = np.array(
+                    [
+                        robot_obs["imu.quat.w"],
+                        robot_obs["imu.quat.x"],
+                        robot_obs["imu.quat.y"],
+                        robot_obs["imu.quat.z"],
+                    ],
+                    np.float64,
+                )
+            else:
+                q = robot_obs.get("imu.quaternion")
+                if q is not None:
+                    q = np.array(q, np.float64)
+            if q is not None:
+                self.heading_init_base_quat = np.array(q, np.float64)
+                with self.motion_lock:
+                    rf = min(self.ref_cursor, self.motion_timesteps - 1)
+                    if self.encode_mode == 2 and self.smpl_root_quat is not None:
+                        # Anchor the heading delta to the SMPL root at init so the
+                        # robot turns *relative* to the clip/operator start heading.
+                        self.init_ref_quat = np.asarray(self.smpl_root_quat, np.float64)
+                    else:
+                        self.init_ref_quat = self.motion_body_quats[rf].copy()
+                self.delta_heading = 0.0
+                self.first_motion = False
+                self.reinit_heading = False
+                logger.debug("[Heading] init quat: %s", self.heading_init_base_quat)
+        return super().step(robot_obs, update_encoder=update_encoder, debug=debug)
+
+    def advance_cursor(self):
+        """Advance the reference cursor one frame per 50 Hz tick (no wall-clock catch-up)."""
+        if not self.playing:
+            return
+        with self.motion_lock:
+            if self.motion_timesteps > 0:
+                self.ref_cursor = min(self.ref_cursor + 1, self.motion_timesteps - 1)

src/lerobot/robots/unitree_g1/controllers/sonic_whole_body.py

@@ -0,0 +1,415 @@
+#!/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.
+
+"""SONIC full-body controller for Unitree G1."""
+
+from __future__ import annotations
+
+from collections import deque
+import logging
+from typing import TYPE_CHECKING
+
+from huggingface_hub import hf_hub_download
+import numpy as np
+
+from lerobot.utils.import_utils import _onnxruntime_available, require_package
+
+from ..g1_utils import (
+    MUJOCO_TO_ISAACLAB,
+    WB_ACTION_DIM,
+    G1_29_JointIndex,
+    lowstate_to_obs,
+    wb_action_key,
+)
+from .sonic_pipeline import (
+    CONTROL_DT,
+    DEFAULT_ANGLES,
+    ENCODER_UPDATE_EVERY,
+    TOKEN_DIM,
+    PlannerController,
+    compute_kp_kd,
+    make_ort_session_options,
+    ort_providers,
+)
+
+# Action-feature prefix for the latent-token interface (see _extract_token_from_action).
+TOKEN_ACTION_PREFIX = "motion_token"
+# Proprio-state prefix for the token interface: the robot echoes the last commanded
+# token here so ``lerobot-rollout`` aggregates it into a 64-D ``observation.state``.
+TOKEN_STATE_PREFIX = "motion_token_state"
+
+
+def token_action_key(i: int) -> str:
+    """Action-dict key for the i-th component of the 64-D SONIC latent token.
+
+    The ``.pos`` suffix is required so the value flows through ``lerobot-rollout``,
+    which only routes ``.pos`` scalar features onto the policy action vector.
+    """
+    return f"{TOKEN_ACTION_PREFIX}.{i}.pos"
+
+
+def token_state_key(i: int) -> str:
+    """Observation key for the i-th component of the 64-D SONIC latent token state."""
+    return f"{TOKEN_STATE_PREFIX}.{i}.pos"
+
+if TYPE_CHECKING or _onnxruntime_available:
+    import onnxruntime as ort
+else:
+    ort = None
+
+logger = logging.getLogger(__name__)
+
+# Startup blend duration: over the first control ticks, linearly interpolate every joint
+# from the robot's initial measured pose into the policy's commanded target, so control
+# eases in without a snap on the first command.
+INIT_RAMP_S = 3.0
+
+# Neutral ("zero pose") SONIC token, held by token_mode until the first real token
+# arrives. Captured from the encoder's own output while the robot stood idle in sim
+# (capture_neutral_token.py): the encoder is an FSQ bottleneck (~5 bit/dim, 15.5 half-
+# width, Div(16)), so its tokens live on the 1/16 grid. We store the integer FSQ codes
+# and rescale by the same 1/16 step, giving an exact on-grid token -- unlike the literal
+# all-zero token, which is off the encoder's learned manifold and decodes to a slightly
+# goofy stance. This one decodes to a stable, natural standing pose.
+_NEUTRAL_TOKEN_CODES = np.array(
+    [-1, 3, 1, -1, 1, -3, 6, 1, 1, 1, -2, -4, -2, 0, -3, -1,
+     2, -1, -3, -5, 3, 1, 1, -4, -1, -1, 1, -7, 0, 1, 2, -2,
+     5, -2, -2, -4, 0, -1, 3, -1, 0, -5, -1, 0, -4, 0, 0, -1,
+     -1, 2, -2, 1, 3, 3, 1, 0, 0, 6, 0, -7, 3, 0, 2, -2],
+    dtype=np.float32,
+)
+NEUTRAL_TOKEN = _NEUTRAL_TOKEN_CODES / 16.0  # FSQ Div(16): integer codes -> on-grid token
+
+
+def _extract_wb34_from_action(action: dict | None) -> np.ndarray | None:
+    """Reassemble a dense (34,) whole-body command from ``wb.{i}.pos`` keys, or None.
+
+    This is the OpenHLM / pi0.5 joint-based interface: one 34-D vector per tick
+    (sentinel: presence of ``wb.0.pos``) carrying absolute joint targets in real
+    units. The ``.pos`` suffix lets these flow through ``lerobot-rollout`` as normal
+    joint-position action features.
+    """
+    if not action:
+        return None
+    keys = [wb_action_key(i) for i in range(WB_ACTION_DIM)]
+    # Require the full dense command: a partial action (e.g. only ``wb.0.pos``)
+    # must not be silently zero-filled, which would drive most joints toward 0.
+    if any(key not in action for key in keys):
+        return None
+    return np.fromiter(
+        (float(action[key]) for key in keys),
+        dtype=np.float32,
+        count=WB_ACTION_DIM,
+    )
+
+
+def _extract_token_from_action(action: dict | None) -> np.ndarray | None:
+    """Reassemble a dense (64,) latent token from ``motion_token.{i}`` keys, or None.
+
+    This is the token-only replay interface: instead of a joint reference driving the
+    encoder, the caller supplies the 64-D encoder latent directly (e.g. a recorded
+    ``action.motion_token`` column), which the decoder consumes with the encoder
+    bypassed. Requires the full dense token; a partial one is ignored (returns None).
+    """
+    if not action:
+        return None
+    keys = [token_action_key(i) for i in range(TOKEN_DIM)]
+    if any(key not in action for key in keys):
+        return None
+    return np.fromiter(
+        (float(action[key]) for key in keys),
+        dtype=np.float32,
+        count=TOKEN_DIM,
+    )
+
+
+def _wb34_to_reference(wb: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """Map a 34-D OpenHLM whole-body command to a SONIC mode-0 reference.
+
+    Returns ``(ref29, anchor_quat)`` where ``ref29`` is the 29 joint targets in
+    IsaacLab order (what SONIC's ``motion_joint_positions`` expects) and
+    ``anchor_quat`` (wxyz) encodes the root roll/pitch (yaw=0).
+
+    OpenHLM layout : [L-arm 0:7, L-grip 7, R-arm 8:15, R-grip 15,
+                      L-leg 16:22, R-leg 22:28, waist 28:31, root rp+yaw 31:34]
+    The 29 joints are first assembled in MuJoCo / Unitree-SDK order
+    ([L-leg 0:6, R-leg 6:12, waist 12:15, L-arm 15:22, R-arm 22:29] — the
+    ``G1_29_JointIndex`` grouping OpenHLM uses), then permuted to IsaacLab order via
+    ``MUJOCO_TO_ISAACLAB``. Grippers (7, 15) are not part of the 29-DoF SONIC
+    reference, and yaw-rate (33) is integrated into the heading by the caller (it
+    cannot be represented in this static per-tick anchor).
+    """
+    ref_mj = np.zeros(29, np.float32)  # MuJoCo / Unitree-SDK grouped order
+    ref_mj[0:6] = wb[16:22]  # left leg
+    ref_mj[6:12] = wb[22:28]  # right leg
+    ref_mj[12:15] = wb[28:31]  # waist
+    ref_mj[15:22] = wb[0:7]  # left arm
+    ref_mj[22:29] = wb[8:15]  # right arm
+    ref = ref_mj[MUJOCO_TO_ISAACLAB].astype(np.float32)  # -> IsaacLab order for SONIC
+    roll, pitch = float(wb[31]), float(wb[32])
+    cr, sr, cp, sp = np.cos(roll / 2), np.sin(roll / 2), np.cos(pitch / 2), np.sin(pitch / 2)
+    anchor = np.array([cr * cp, sr * cp, cr * sp, sr * sp], np.float32)  # Rx(roll)·Ry(pitch)
+    return ref, anchor
+
+
+class SonicRuntime:
+    """Loads the SONIC encoder/decoder ONNX models and owns the controller.
+
+    No motion planner: the reference motion buffer is written directly each tick by
+    :class:`SonicWholeBodyController` from the incoming 34-D whole-body command.
+    """
+
+    def __init__(self, force_cpu: bool = False):
+        require_package("onnxruntime", extra="unitree_g1")
+        encoder_path = hf_hub_download(repo_id="nvidia/GEAR-SONIC", filename="model_encoder.onnx")
+        decoder_path = hf_hub_download(repo_id="nvidia/GEAR-SONIC", filename="model_decoder.onnx")
+
+        providers = ort_providers(force_cpu=force_cpu)
+        so = make_ort_session_options()
+
+        encoder_sess = ort.InferenceSession(encoder_path, sess_options=so, providers=providers)
+        decoder_sess = ort.InferenceSession(decoder_path, sess_options=so, providers=providers)
+
+        # Report the provider actually bound, not the one requested: ORT silently falls
+        # back to CPU if CUDA can't load (e.g. libcudnn not on LD_LIBRARY_PATH), and a
+        # CPU decoder drifts the closed-loop heading. Warn loudly so it can't hide.
+        self.use_gpu = decoder_sess.get_providers()[0] == "CUDAExecutionProvider"
+        if not force_cpu and not self.use_gpu:
+            print(
+                "[SONIC] WARNING: decoder bound to CPUExecutionProvider (CUDA unavailable). "
+                "Closed-loop replay/control will drift. Ensure libcudnn is on LD_LIBRARY_PATH "
+                "(site-packages/nvidia/*/lib).",
+                flush=True,
+            )
+
+        self.kp, self.kd = compute_kp_kd()
+        self.controller = PlannerController(encoder_sess, decoder_sess)
+
+    @property
+    def pipeline(self):
+        return self.controller
+
+    def reset(self):
+        # Full pipeline reset: clears the encoder token, proprioception history and
+        # heading, and rewinds the motion buffer. reinit_heading is set so the next
+        # step re-latches the reference frame to the current robot orientation.
+        self.controller.reset()
+
+    def shutdown(self):
+        pass
+
+
+class SonicWholeBodyController:
+    """Full-body SONIC controller for UnitreeG1's background controller thread."""
+
+    control_dt = CONTROL_DT
+    full_body = True
+    # Advertise a dense 34-D whole-body action space (OpenHLM / pi0.5) so the robot
+    # exposes ``wb.{i}.pos`` action features and ``lerobot-rollout`` can drive it
+    # directly with a 34-D VLA policy.
+    wb_action = True
+
+    def __init__(self, force_cpu: bool = False):
+        logger.info("Loading SONIC whole-body controller...")
+        self._runtime = SonicRuntime(force_cpu=force_cpu)
+        self.kp = self._runtime.kp
+        self.kd = self._runtime.kd
+        self.controller = self._runtime.controller
+
+        # Startup blend: ease from the robot's initial pose into the first commanded
+        # policy targets over INIT_RAMP_S (captured on the first control tick).
+        self._init_ramp_steps = max(1, round(INIT_RAMP_S / CONTROL_DT))
+        self._init_step = 0
+        self._start_pose: dict[str, float] = {}
+
+        # Tick counter for the dense whole-body (OpenHLM, mode-0) path's encoder cadence.
+        self._wb_step = 0
+        # Rolling 50-frame reference trajectory (ref29 + anchor quat) built from the
+        # stream of per-tick whole-body commands, fed to the encoder as a batch.
+        self._wb_traj: deque[np.ndarray] = deque(maxlen=50)
+        self._wb_quat_traj: deque[np.ndarray] = deque(maxlen=50)
+        # Integrated heading (rad) from the whole-body command's yaw-rate (index 33),
+        # forwarded to the pipeline as ``delta_heading`` so turn commands take effect.
+        self._heading = 0.0
+
+        # Token-interface state. ``token_mode`` is set True by the robot when the deploy
+        # is token-driven (``UnitreeG1Config.sonic_token_action``): the controller then
+        # holds a stable *neutral* (all-zero) token until the first real token arrives,
+        # and afterwards holds the *last* token received between ticks (the async
+        # controller runs ~50 Hz while a token VLA streams ~30 Hz). This lives here (not
+        # in the entry-point script) so it applies uniformly to run_g1_onboard,
+        # lerobot-rollout and the sim replays. ``token_mode`` stays False for the dense
+        # 34-D whole-body / OpenHLM path, which keeps its own "hold last target" idle.
+        self.token_mode = False
+        self._last_token: np.ndarray | None = None
+
+        logger.info("SONIC ready (encoder/decoder, 34-D whole-body command path)")
+
+    def _run_wholebody34(self, obs: dict, wb: np.ndarray) -> dict:
+        """Feed a dense 34-D OpenHLM whole-body command as the mode-0 encoder reference.
+
+        The 29 joint targets are held across the encoder lookahead window (zero
+        velocity) and the root roll/pitch set the anchor orientation, then the
+        encoder/decoder run directly (planner bypassed). One command per tick, so the
+        VLA's commanded pose is what SONIC tracks.
+        """
+        ref, anchor = _wb34_to_reference(wb)
+        c = self.controller
+        if c.encode_mode != 0:
+            c.encode_mode = 0
+            c.reinit_heading = True
+        # Index 33 is a yaw-rate (rad/s): integrate it into a heading offset and hand
+        # it to the pipeline as ``delta_heading`` so commanded turns are tracked rather
+        # than silently dropped (the anchor from _wb34_to_reference only carries r/p).
+        self._heading += float(wb[33]) * CONTROL_DT
+        c.delta_heading = self._heading
+        # Capture the heading/anchor reference on the first whole-body tick. The
+        # controller only latches ``init_ref_quat`` (and the base heading) inside
+        # ``step()`` when ``first_motion or reinit_heading`` — but it already boots in
+        # mode 0, so the mode-switch guard above misses the very first command and the
+        # anchor would stay identity. This mirrors the GEAR reference, which seeds
+        # ``init_ref_quat`` from the first anchor. Must run before the buffers below so
+        # ``step()`` latches ``motion_body_quats[0]`` = this tick's anchor.
+        if self._wb_step == 0:
+            c.reinit_heading = True
+
+        # Accumulate the per-tick commands into a rolling 50-frame reference
+        # trajectory so the encoder's 10-frame, step-5 lookahead sees an actual
+        # motion sequence (with velocities) instead of one repeated pose. 50 frames
+        # == chunk horizon == 10 lookahead frames × step 5.
+        self._wb_traj.append(ref)
+        self._wb_quat_traj.append(anchor)
+        traj = np.asarray(self._wb_traj, np.float32)  # (L, 29), oldest -> newest
+        quats = np.asarray(self._wb_quat_traj, np.float32)  # (L, 4)
+        n = len(traj)
+        # Per-frame velocities from finite differences (rad/s at the control rate).
+        vel = np.zeros_like(traj)
+        if n > 1:
+            vel[1:] = (traj[1:] - traj[:-1]) / CONTROL_DT
+            vel[0] = vel[1]
+        with c.motion_lock:
+            c.motion_joint_positions[:n] = traj
+            c.motion_joint_velocities[:n] = vel
+            c.motion_body_quats[:n] = quats
+            c.motion_body_pos[:n] = 0.0
+            c.motion_timesteps = n
+            c.ref_cursor = 0
+        c.playing = True
+        do_enc = self._wb_step % ENCODER_UPDATE_EVERY == 0
+        out = c.step(obs, update_encoder=do_enc, debug=False)
+        if self._wb_step % 25 == 0:
+            tgt = np.array([out[f"{m.name}.q"] for m in G1_29_JointIndex], np.float32)
+            logger.info(
+                "[WB34] step=%d |ref|mean=%.3f |target|mean=%.3f target_std=%.3f init_ref_quat=%s",
+                self._wb_step,
+                float(np.abs(ref).mean()),
+                float(np.abs(tgt).mean()),
+                float(tgt.std()),
+                np.round(c.init_ref_quat, 3).tolist(),
+            )
+        self._wb_step += 1
+        return out
+
+    def _run_token(self, obs: dict, token: np.ndarray) -> dict:
+        """Decode a supplied 64-D latent token directly (encoder bypassed).
+
+        Token-only replay: set the pipeline's cached token to the supplied one and run
+        a decode-only step (``update_encoder=False``). The decoder still closes the loop
+        on live proprioception (history is refreshed inside ``step`` from ``obs``); only
+        the encoder — which would recompute the token from a motion reference — is
+        skipped. Returns the ``<joint>.q`` target dict.
+        """
+        c = self.controller
+        c.token = np.asarray(token, np.float32)
+        self._wb_step += 1
+        return c.step(obs, update_encoder=False, debug=False)
+
+    def _startup_blend(self, obs: dict, out: dict) -> dict:
+        """Ease into policy control at startup: for the first ``INIT_RAMP_S`` seconds,
+        interpolate between the robot's pose captured on the first tick and the policy's
+        live commanded target, so the handoff has no snap.
+
+        ``out`` is the policy's ``<joint>.q`` target dict for this tick; the blend ratio
+        climbs 0->1 over the ramp, after which the raw policy target passes through.
+        """
+        if self._init_step >= self._init_ramp_steps or not out:
+            return out
+        if self._init_step == 0:
+            # Capture the robot's actual pose as the interpolation start point.
+            self._start_pose = {
+                f"{m.name}.q": float(obs.get(f"{m.name}.q", DEFAULT_ANGLES[m.value]))
+                for m in G1_29_JointIndex
+            }
+        self._init_step += 1
+        ratio = min(1.0, self._init_step / self._init_ramp_steps)
+        blended = {
+            k: self._start_pose.get(k, float(tgt)) * (1.0 - ratio) + float(tgt) * ratio
+            for k, tgt in out.items()
+        }
+        if self._init_step >= self._init_ramp_steps:
+            logger.info("SONIC startup blend complete -> full policy control")
+        return blended
+
+    def run_step(self, action: dict, lowstate) -> dict:
+        if lowstate is None:
+            return {}
+        obs = lowstate_to_obs(lowstate)
+
+        # Token-only interface (latent replay / token-output VLA): a dense 64-D
+        # ``motion_token.{i}`` command is decoded directly, bypassing the encoder.
+        # Checked before the joint path so a token action takes precedence.
+        token = _extract_token_from_action(action)
+        if token is not None:
+            self._last_token = token
+        elif self._last_token is None and self.token_mode:
+            # Token-driven deploy, but no token has arrived yet: hold the captured
+            # neutral token (NEUTRAL_TOKEN), which the decoder maps to a stable, natural
+            # standing pose (the encoder's own idle output; see NEUTRAL_TOKEN).
+            self._last_token = NEUTRAL_TOKEN.copy()
+        if self._last_token is not None:
+            # Either a fresh token this tick or the last one received (held between the
+            # ~30 Hz token stream and the ~50 Hz control loop).
+            return self._startup_blend(obs, self._run_token(obs, self._last_token))
+
+        # Dense 34-D whole-body command (OpenHLM / pi0.5 joint interface): a single
+        # vector per tick drives the mode-0 encoder reference directly. Until the
+        # policy produces one, hold (no command) so the robot keeps its last target.
+        wb = _extract_wb34_from_action(action)
+        if wb is None:
+            self._wb_miss = getattr(self, "_wb_miss", 0) + 1
+            if self._wb_miss % 50 == 1:
+                akeys = [k for k in action if isinstance(k, str)]
+                logger.info(
+                    "[WB34] no wb.*.pos in action this tick (miss=%d). action keys sample: %s",
+                    self._wb_miss,
+                    akeys[:8],
+                )
+            return {}
+        return self._startup_blend(obs, self._run_wholebody34(obs, wb))
+
+    def reset(self):
+        self._runtime.reset()
+        self._init_step = 0  # re-run the startup blend after a reset
+        self._start_pose = {}
+        self._wb_step = 0
+        self._wb_traj.clear()
+        self._wb_quat_traj.clear()
+        self._heading = 0.0
+        # Drop the held token so token_mode re-seeds the neutral token after a reset.
+        self._last_token = None
+
+    def shutdown(self):
+        self._runtime.shutdown()

src/lerobot/robots/unitree_g1/g1_utils.py

@@ -23,10 +23,102 @@ import numpy as np
 
 NUM_MOTORS = 29
 
+# Joint-order permutations between the two 29-DoF layouts used across the G1 stack:
+# IsaacLab (policy/training order) and MuJoCo (deploy order). ``a[ISAACLAB_TO_MUJOCO]``
+# reorders an IsaacLab-ordered vector into MuJoCo order, and vice-versa.
+ISAACLAB_TO_MUJOCO = np.array(
+    [
+        0,
+        3,
+        6,
+        9,
+        13,
+        17,
+        1,
+        4,
+        7,
+        10,
+        14,
+        18,
+        2,
+        5,
+        8,
+        11,
+        15,
+        19,
+        21,
+        23,
+        25,
+        27,
+        12,
+        16,
+        20,
+        22,
+        24,
+        26,
+        28,
+    ],
+    dtype=np.int32,
+)
+MUJOCO_TO_ISAACLAB = np.array(
+    [
+        0,
+        6,
+        12,
+        1,
+        7,
+        13,
+        2,
+        8,
+        14,
+        3,
+        9,
+        15,
+        22,
+        4,
+        10,
+        16,
+        23,
+        5,
+        11,
+        17,
+        24,
+        18,
+        25,
+        19,
+        26,
+        20,
+        27,
+        21,
+        28,
+    ],
+    dtype=np.int32,
+)
+
 REMOTE_AXES = ("remote.lx", "remote.ly", "remote.rx", "remote.ry")
 REMOTE_BUTTONS = tuple(f"remote.button.{i}" for i in range(16))
 REMOTE_KEYS = REMOTE_AXES + REMOTE_BUTTONS
 
+# Reserved action-dict field used to forward the set of currently-pressed keyboard
+# keys from a KeyboardTeleop through the standard action pipeline to the SONIC
+# whole-body controller (see SonicWholeBodyController._process_keyboard).
+KEYBOARD_KEYS_FIELD = "keyboard.keys"
+
+# ── Dense whole-body joint reference (SONIC encode_mode 0, OpenHLM / pi0.5) ──────
+# A single 34-D whole-body command per tick, in the OpenHLM action layout:
+#   [L-arm(7), L-grip(1), R-arm(7), R-grip(1), L-leg(6), R-leg(6), waist(3),
+#    root roll/pitch + yaw-rate(3)]
+# Fed as flat scalars ``wb.0.pos .. wb.33.pos``. The ``.pos`` suffix makes these
+# behave like ordinary joint-position action features so ``lerobot-rollout`` routes
+# them straight from a 34-D VLA (OpenHLM / pi0.5) onto the robot.
+WB_ACTION_PREFIX = "wb."
+WB_ACTION_DIM = 34
+
+
+def wb_action_key(i: int) -> str:
+    """Action-dict key for the ``i``-th whole-body command scalar (``wb.{i}.pos``)."""
+    return f"{WB_ACTION_PREFIX}{i}.pos"
+
 
 def default_remote_input() -> dict[str, float]:
     """Return a zeroed-out remote input dict (axes + buttons)."""
@@ -63,13 +155,92 @@ class G1_29_JointArmIndex(IntEnum):
     kRightWristYaw = 28
 
 
+def lowstate_to_obs(lowstate) -> dict:
+    """Build a robot observation dict from a Unitree lowstate.
+
+    Shared by ``UnitreeG1.get_observation`` and the SONIC pipeline so the
+    lowstate -> obs mapping lives in exactly one place. Keys match the
+    ``<joint>.q``/``imu.*`` schema consumed across the controllers.
+    """
+    obs: dict = {}
+
+    for motor in G1_29_JointIndex:
+        idx = motor.value
+        obs[f"{motor.name}.q"] = lowstate.motor_state[idx].q
+        obs[f"{motor.name}.dq"] = lowstate.motor_state[idx].dq
+        obs[f"{motor.name}.tau"] = lowstate.motor_state[idx].tau_est
+
+    imu = lowstate.imu_state
+    if imu.gyroscope:
+        obs["imu.gyro.x"] = imu.gyroscope[0]
+        obs["imu.gyro.y"] = imu.gyroscope[1]
+        obs["imu.gyro.z"] = imu.gyroscope[2]
+    if imu.accelerometer:
+        obs["imu.accel.x"] = imu.accelerometer[0]
+        obs["imu.accel.y"] = imu.accelerometer[1]
+        obs["imu.accel.z"] = imu.accelerometer[2]
+    if imu.quaternion:
+        obs["imu.quat.w"] = imu.quaternion[0]
+        obs["imu.quat.x"] = imu.quaternion[1]
+        obs["imu.quat.y"] = imu.quaternion[2]
+        obs["imu.quat.z"] = imu.quaternion[3]
+    if imu.rpy:
+        obs["imu.rpy.roll"] = imu.rpy[0]
+        obs["imu.rpy.pitch"] = imu.rpy[1]
+        obs["imu.rpy.yaw"] = imu.rpy[2]
+
+    wr = getattr(lowstate, "wireless_remote", None)
+    if wr:
+        obs["wireless_remote"] = bytes(wr) if not isinstance(wr, (bytes, bytearray)) else wr
+
+    return obs
+
+
+def obs_to_wb34_state(obs: dict) -> np.ndarray:
+    """Build the 34-D OpenHLM / pi0.5 proprio state from a G1 observation dict.
+
+    Mirrors the whole-body *action* layout so the policy sees state and action in
+    the same coordinates::
+
+        [L-arm(7), L-grip(1), R-arm(7), R-grip(1),
+         L-leg(6), R-leg(6), waist(3), root roll/pitch + yaw-rate(3)]
+
+    Joint positions come from the ``<joint>.q`` obs keys, which are already in
+    MuJoCo / Unitree-SDK order — the same body-part grouping OpenHLM uses
+    ([L-leg 0:6, R-leg 6:12, waist 12:15, L-arm 15:22, R-arm 22:29]) — so they are
+    regrouped directly (no IsaacLab permutation). The G1 has no grippers in its
+    29-DoF body, so both gripper slots are 0. Root roll/pitch are the IMU RPY and
+    the last slot is the IMU yaw rate (gyro z).
+    """
+    q_mj = np.array(
+        [float(obs.get(f"{m.name}.q", 0.0)) for m in G1_29_JointIndex],
+        dtype=np.float32,
+    )
+    lleg, rleg, waist = q_mj[0:6], q_mj[6:12], q_mj[12:15]
+    larm, rarm = q_mj[15:22], q_mj[22:29]
+
+    state = np.zeros(34, dtype=np.float32)
+    state[0:7] = larm
+    # state[7] left gripper — none on 29-DoF G1
+    state[8:15] = rarm
+    # state[15] right gripper — none on 29-DoF G1
+    state[16:22] = lleg
+    state[22:28] = rleg
+    state[28:31] = waist
+    state[31] = float(obs.get("imu.rpy.roll", 0.0))
+    state[32] = float(obs.get("imu.rpy.pitch", 0.0))
+    state[33] = float(obs.get("imu.gyro.z", 0.0))
+    return state
+
+
 def make_locomotion_controller(name: str | None):
     """Instantiate a locomotion controller by class name. Returns None if name is None."""
     if name is None:
         return None
     controllers = {
-        "GrootLocomotionController": "lerobot.robots.unitree_g1.gr00t_locomotion",
+        "GrootLocomotionController": "lerobot.robots.unitree_g1.controllers.gr00t_locomotion",
-        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.holosoma_locomotion",
+        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.controllers.holosoma_locomotion",
+        "SonicWholeBodyController": "lerobot.robots.unitree_g1.controllers.sonic_whole_body",
     }
     module_path = controllers.get(name)
     if module_path is None:

src/lerobot/robots/unitree_g1/infer_sonic_g1_onboard.py

@@ -0,0 +1,192 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Laptop-side sender for the SONIC whole-body walk policy, onboard deployment.
+
+This is the counterpart to ``run_g1_onboard.py`` (which runs the SONIC decoder on the
+robot). The heavy VLA (``nepyope/sonic_walk``, a pi0.5 token policy) runs here on the
+laptop GPU; only the resulting 64-D latent token is shipped to the robot over ZMQ:
+
+    laptop:  camera frame (ZMQ from robot :5555) + previous token
+             -> pi0.5 -> next 64-D token
+             -> PUSH JSON {motion_token.i.pos: ...} to robot :6004
+    robot:   run_g1_onboard receives the token, SonicWholeBodyController decodes it
+             into whole-body joint commands against local DDS at full rate.
+
+The policy's ``observation.state`` is the token currently being executed, so we close
+the loop by feeding back the *last token we sent* (the decoder holds it until a new one
+arrives). This mirrors what ``lerobot-rollout`` does via the robot's token echo, but
+without a controller / DDS on the laptop.
+
+The policy is pi0.5 with chunk_size=50, so a full diffusion inference runs only about
+once every 50 ticks; ``select_action`` pops one queued token per tick in between.
+
+Run ``run_g1_onboard.py --controller SonicWholeBodyController --sonic-token-action
+--cameras ...`` on the robot first, then this on the laptop:
+
+    python -m lerobot.robots.unitree_g1.infer_sonic_g1_onboard \
+        --policy-path nepyope/sonic_walk --robot-ip 192.168.123.164 \
+        --task "walk back and forth"
+"""
+
+import argparse
+import contextlib
+import json
+import logging
+import signal
+import time
+
+import numpy as np
+import torch
+
+from lerobot.cameras.zmq import ZMQCamera, ZMQCameraConfig
+from lerobot.configs.policies import PreTrainedConfig
+from lerobot.policies.factory import get_policy_class, make_pre_post_processors
+from lerobot.policies.utils import prepare_observation_for_inference
+from lerobot.robots.unitree_g1.controllers.sonic_whole_body import (
+    NEUTRAL_TOKEN,
+    TOKEN_DIM,
+    token_action_key,
+)
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", force=True)
+logger = logging.getLogger("sonic_sender")
+
+ACTION_PORT = 6004  # matches run_g1_onboard.py --action-port
+IMAGE_KEY = "observation.images.ego_view"  # pi05 sonic_walk VISUAL input
+STATE_KEY = "observation.state"
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--policy-path", default="nepyope/sonic_walk", help="Policy repo id or local path")
+    p.add_argument("--robot-ip", default="192.168.123.164", help="Robot IP (camera + action ports)")
+    p.add_argument("--action-port", type=int, default=ACTION_PORT, help="Onboard ZMQ PULL port for actions")
+    p.add_argument("--camera-port", type=int, default=5555, help="Onboard ZMQ camera PUB port")
+    p.add_argument("--camera-name", default="head_camera", help="Camera name served by run_g1_onboard")
+    p.add_argument("--camera-width", type=int, default=640, help="Camera width")
+    p.add_argument("--camera-height", type=int, default=480, help="Camera height")
+    p.add_argument("--task", default="walk back and forth", help="Language prompt for the VLA")
+    p.add_argument("--fps", type=float, default=30.0, help="Token send rate (matches training inference)")
+    p.add_argument("--device", default="cuda", help="Torch device")
+    p.add_argument("--max-ticks", type=int, default=0, help="Stop after N ticks (0 = run forever)")
+    p.add_argument("--dry-run", action="store_true", help="Run inference but do not PUSH tokens to the robot")
+    args = p.parse_args()
+
+    device = torch.device(args.device)
+
+    # --- Policy + processors (normalization stats baked into the checkpoint) ---
+    logger.info("Loading policy from '%s'...", args.policy_path)
+    policy_cfg = PreTrainedConfig.from_pretrained(args.policy_path)
+    policy_cfg.pretrained_path = args.policy_path
+    policy = get_policy_class(policy_cfg.type).from_pretrained(args.policy_path, config=policy_cfg)
+    policy = policy.to(device)
+    policy.eval()
+    policy.reset()
+
+    preprocessor, postprocessor = make_pre_post_processors(
+        policy_cfg=policy_cfg,
+        pretrained_path=args.policy_path,
+        preprocessor_overrides={"device_processor": {"device": str(device)}},
+    )
+    logger.info("Policy loaded (type=%s, device=%s, chunk=%s)", policy_cfg.type, device,
+                getattr(policy_cfg, "chunk_size", "?"))
+
+    # --- Camera (ZMQ from the robot's onboard image server) ---
+    cam = ZMQCamera(
+        ZMQCameraConfig(
+            server_address=args.robot_ip,
+            port=args.camera_port,
+            camera_name=args.camera_name,
+            width=args.camera_width,
+            height=args.camera_height,
+            fps=int(args.fps),
+        )
+    )
+    logger.info("Connecting camera %s@%s:%d ...", args.camera_name, args.robot_ip, args.camera_port)
+    cam.connect()
+
+    # --- Action PUSH socket to the onboard controller ---
+    import zmq
+
+    ctx = zmq.Context.instance()
+    sock = ctx.socket(zmq.PUSH)
+    sock.setsockopt(zmq.SNDHWM, 2)
+    sock.setsockopt(zmq.LINGER, 0)
+    sock.connect(f"tcp://{args.robot_ip}:{args.action_port}")
+    logger.info("Sending tokens to tcp://%s:%d (dry_run=%s)", args.robot_ip, args.action_port, args.dry_run)
+
+    stop = {"flag": False}
+    signal.signal(signal.SIGINT, lambda *_: stop.__setitem__("flag", True))
+    signal.signal(signal.SIGTERM, lambda *_: stop.__setitem__("flag", True))
+
+    # observation.state = the token currently executing on the robot (last one we sent);
+    # start at the neutral token the decoder holds before the first send, so the very
+    # first inference sees the true executing token (not zeros).
+    prev_token = NEUTRAL_TOKEN.copy()
+    period = 1.0 / args.fps
+    n = 0
+    t_infer_total = 0.0
+    logger.info("Streaming tokens at %.0f Hz. Ctrl-C to stop.", args.fps)
+    try:
+        while not stop["flag"]:
+            t0 = time.time()
+            try:
+                frame = cam.read()  # HxWxC uint8 RGB
+            except Exception as e:  # noqa: BLE001
+                logger.warning("Camera read failed: %s", e)
+                time.sleep(period)
+                continue
+
+            raw_obs = {
+                IMAGE_KEY: np.ascontiguousarray(frame),
+                STATE_KEY: prev_token.copy(),
+            }
+            with torch.inference_mode():
+                obs = prepare_observation_for_inference(raw_obs, device, args.task, "unitree_g1")
+                obs = preprocessor(obs)
+                action = policy.select_action(obs)
+                action = postprocessor(action)
+            token = action.squeeze(0).to("cpu").numpy().astype(np.float32)
+            prev_token = token
+
+            if not args.dry_run:
+                msg = {token_action_key(i): float(token[i]) for i in range(TOKEN_DIM)}
+                with contextlib.suppress(zmq.Again):
+                    sock.send_string(json.dumps(msg), zmq.NOBLOCK)
+
+            n += 1
+            t_infer_total += time.time() - t0
+            if n % 30 == 0:
+                logger.info(
+                    "tick %d | avg %.1f ms/tick | token[:3]=%s",
+                    n, 1000.0 * t_infer_total / 30.0, np.round(token[:3], 3).tolist(),
+                )
+                t_infer_total = 0.0
+
+            if args.max_ticks and n >= args.max_ticks:
+                break
+            time.sleep(max(0.0, period - (time.time() - t0)))
+    finally:
+        logger.info("Stopping sender after %d ticks.", n)
+        with contextlib.suppress(Exception):
+            cam.disconnect()
+        with contextlib.suppress(Exception):
+            sock.close(linger=0)
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/robots/unitree_g1/run_g1_onboard.py

@@ -0,0 +1,254 @@
+#!/usr/bin/env python
+
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Run the G1 locomotion / whole-body controller ONBOARD, driven by high-level actions
+from a laptop.
+
+The controller (GR00T / Holosoma / SONIC whole-body) runs on the robot itself against
+local DDS, at full control rate. The laptop ships only the resulting high-level action
+(arm joint targets + joystick axes + gripper flags, or a 64-D SONIC motion token) as
+JSON over ZMQ. This process applies each action via ``UnitreeG1.send_action`` while the
+onboard controller thread keeps the legs balanced / decodes the token.
+
+This is the real-deploy counterpart to running ``lerobot-rollout`` on the laptop with
+``--robot.is_simulation=false`` (the ZMQ *socket bridge*): there the 50 Hz lowcmd
+crosses the network; here only compact high-level actions do, and the control loop stays
+local to the robot. Pair with a laptop client that produces actions (exo teleop, or a
+policy such as ``nepyope/sonic_walk`` emitting ``motion_token.{i}.pos``).
+
+Besides receiving actions, this process publishes ``observation.state`` (29 joint ``.q``)
+on a ZMQ PUB port so a laptop policy client has proprioception.
+
+Safety: type ``e`` then Enter in this terminal to stop immediately (zero-torque + exit).
+Ctrl-C does the normal graceful shutdown (kp ramp).
+
+Examples (on the robot):
+
+    # GR00T locomotion, arm targets from the laptop:
+    python -m lerobot.robots.unitree_g1.run_g1_onboard --controller GrootLocomotionController
+
+    # SONIC whole-body walk policy: laptop ships 64-D tokens, decoder runs here:
+    python -m lerobot.robots.unitree_g1.run_g1_onboard \
+        --controller SonicWholeBodyController --sonic-token-action \
+        --cameras "head_camera:/dev/v4l/by-path/platform-3610000.usb-usb-0:2.1:1.3-video-index0:640x480"
+"""
+
+import argparse
+import contextlib
+import json
+import logging
+import os
+import signal
+import sys
+import threading
+import time
+
+import numpy as np
+import zmq
+
+from lerobot.cameras.zmq.image_server import ImageServer
+from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
+from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
+from lerobot.robots.unitree_g1.run_g1_server import Gripper, build_gripper, parse_camera_specs
+from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s", force=True)
+logger = logging.getLogger("g1_onboard")
+
+ACTION_PORT = 6004
+STATE_PORT = 6005
+
+
+def main() -> None:
+    p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    p.add_argument("--controller", default="GrootLocomotionController", help="Controller class name")
+    p.add_argument("--dds-interface", default=None, help="DDS network interface (default: SDK default)")
+    p.add_argument(
+        "--sim",
+        action="store_true",
+        help="Attach to a DDS MuJoCo sim: skip MotionSwitcher + physical remote, default dds-interface 'lo'.",
+    )
+    p.add_argument(
+        "--sonic-token-action",
+        action="store_true",
+        help="SONIC token interface: actions carry a 64-D motion_token.{i}.pos that the decoder consumes.",
+    )
+    p.add_argument("--action-port", type=int, default=ACTION_PORT, help="ZMQ PULL port for laptop actions")
+    p.add_argument("--state-port", type=int, default=STATE_PORT, help="ZMQ PUB port for observation.state")
+    p.add_argument("--state-fps", type=float, default=30.0, help="observation.state publish rate; <=0 disables")
+    p.add_argument("--gravity-compensation", action="store_true", help="Enable arm gravity compensation")
+    # Gripper control (Damiao over CAN).
+    p.add_argument("--grippers", action="store_true", help="Drive Damiao grippers from action L3/R3 flags")
+    p.add_argument("--gripper-port-left", default="can1", help="CAN interface for LEFT gripper")
+    p.add_argument("--gripper-port-right", default="can0", help="CAN interface for RIGHT gripper")
+    p.add_argument("--gripper-send-id", type=lambda x: int(x, 0), default=0x08, help="Motor send CAN id")
+    p.add_argument("--gripper-recv-id", type=lambda x: int(x, 0), default=0x18, help="Motor recv CAN id")
+    p.add_argument("--gripper-motor-type", default="dm4310", help="Damiao motor type")
+    p.add_argument("--gripper-open-deg", type=float, default=-65.0, help="Gripper OPEN position (deg)")
+    p.add_argument("--gripper-close-deg", type=float, default=0.0, help="Gripper CLOSE position (deg)")
+    p.add_argument("--gripper-kp", type=float, default=15.0, help="MIT position gain (stiffness)")
+    p.add_argument("--gripper-kd", type=float, default=0.5, help="MIT damping gain")
+    p.add_argument("--gripper-no-fd", dest="gripper_fd", action="store_false", help="Classic CAN (non-FD)")
+    p.set_defaults(gripper_fd=True)
+    # Optional camera streaming (ZMQ) so the laptop policy client / viewer can connect.
+    p.add_argument("--cameras", default=None, help="Camera spec 'name:device[:WxH[:FOURCC]]', comma-sep")
+    p.add_argument("--camera-fps", type=int, default=30, help="Camera FPS")
+    p.add_argument("--camera-port", type=int, default=5555, help="Camera ZMQ port")
+    p.add_argument("--camera-width", type=int, default=640, help="Default camera width")
+    p.add_argument("--camera-height", type=int, default=480, help="Default camera height")
+    args = p.parse_args()
+
+    dds_interface = args.dds_interface
+    if args.sim and dds_interface is None:
+        dds_interface = "lo"
+
+    cfg = UnitreeG1Config(
+        is_simulation=False,
+        onboard=True,
+        controller=args.controller,
+        dds_interface=dds_interface,
+        gravity_compensation=args.gravity_compensation,
+        release_motion_control=not args.sim,
+        physical_remote=not args.sim,
+        sonic_token_action=args.sonic_token_action,
+        cameras={},
+    )
+
+    # Optional camera server (background thread; independent of DDS/CAN).
+    camera_server = None
+    if args.cameras:
+        cameras = parse_camera_specs(args.cameras, args.camera_width, args.camera_height)
+        camera_server = ImageServer({"fps": args.camera_fps, "cameras": cameras}, port=args.camera_port)
+        threading.Thread(target=camera_server.run, daemon=True).start()
+        cam_summary = ", ".join(f"{name}(dev {c['device_id']})" for name, c in cameras.items())
+        logger.info("Camera server started on :%d: %s", args.camera_port, cam_summary)
+
+    robot = UnitreeG1(cfg)
+    logger.info("Connecting onboard robot (controller=%s, token=%s)...", args.controller, args.sonic_token_action)
+    robot.connect()
+    # Note: with --sonic-token-action the SonicWholeBodyController holds a neutral
+    # (all-zero) token until the first laptop token arrives, then holds the last token
+    # between ticks -- see SonicWholeBodyController.token_mode (set from config).
+
+    grippers: dict[str, Gripper] = {}
+    if args.grippers:
+        for side, port in (("L", args.gripper_port_left), ("R", args.gripper_port_right)):
+            grippers[side] = build_gripper(
+                side, port, args.gripper_send_id, args.gripper_recv_id, args.gripper_motor_type,
+                args.gripper_fd, args.gripper_open_deg, args.gripper_close_deg, args.gripper_kp, args.gripper_kd,
+            )
+        logger.info("Grippers enabled: L3 -> left, R3 -> right")
+
+    ctx = zmq.Context.instance()
+    sock = ctx.socket(zmq.PULL)
+    sock.setsockopt(zmq.CONFLATE, 1)  # only ever act on the freshest command
+    sock.setsockopt(zmq.RCVTIMEO, 200)  # keeps the loop responsive to the stop event
+    sock.bind(f"tcp://0.0.0.0:{args.action_port}")
+    logger.info("Onboard controller live. Waiting for laptop actions on :%d ...", args.action_port)
+    logger.info("Type 'e' then Enter to STOP immediately (or Ctrl-C for graceful shutdown).")
+
+    stop = threading.Event()
+    signal.signal(signal.SIGINT, lambda *_: stop.set())
+    signal.signal(signal.SIGTERM, lambda *_: stop.set())
+
+    def estop_listener() -> None:
+        for line in sys.stdin:
+            if line.strip().lower() == "e":
+                logger.warning("E-STOP ('e'): going passive NOW.")
+                try:
+                    robot._shutdown_event.set()  # stop the controller loop publishing
+                    time.sleep(0.05)
+                    robot._send_zero_torque()  # motors limp; nothing overwrites it now
+                except Exception as e:  # noqa: BLE001
+                    logger.warning("E-stop zero-torque failed: %s", e)
+                os._exit(0)  # immediate hard exit, no slow cleanup
+
+    threading.Thread(target=estop_listener, daemon=True).start()
+
+    # Proprioception feedback: publish observation.state (29 joint .q) so a laptop
+    # inference client can feed it to a policy. DDS stays local; only compact JSON
+    # state crosses the network. (For a token policy the laptop closes the loop on the
+    # token instead, but publishing joint state is harmless and useful for logging.)
+    state_sock = None
+    if args.state_fps > 0:
+        state_sock = ctx.socket(zmq.PUB)
+        state_sock.setsockopt(zmq.SNDHWM, 2)
+        state_sock.setsockopt(zmq.LINGER, 0)
+        state_sock.bind(f"tcp://0.0.0.0:{args.state_port}")
+        logger.info("Publishing observation.state on :%d at %.0f Hz", args.state_port, args.state_fps)
+
+        def publish_state() -> None:
+            period = 1.0 / args.state_fps
+            joint_names = [j.name for j in G1_29_JointIndex]
+            while not stop.is_set():
+                t0 = time.time()
+                obs = robot.get_observation()
+                if obs:
+                    state = {f"{name}.q": float(obs.get(f"{name}.q", 0.0)) for name in joint_names}
+                    with contextlib.suppress(zmq.Again):
+                        state_sock.send_json(state, zmq.NOBLOCK)
+                time.sleep(max(0.0, period - (time.time() - t0)))
+
+        threading.Thread(target=publish_state, daemon=True).start()
+    else:
+        logger.info("observation.state PUB disabled (--state-fps<=0)")
+
+    n = 0
+    try:
+        while not stop.is_set():
+            try:
+                payload = sock.recv()
+            except zmq.Again:
+                continue
+            except zmq.ContextTerminated:
+                break
+
+            try:
+                action = json.loads(payload.decode("utf-8"))
+            except (ValueError, UnicodeDecodeError) as e:
+                logger.warning("Dropping malformed action: %s", e)
+                continue
+
+            robot.send_action(action)
+
+            if grippers:
+                # L3 = remote.button.4 -> left, R3 = remote.button.0 -> right.
+                if "L" in grippers and "remote.button.4" in action:
+                    grippers["L"].apply(bool(action["remote.button.4"]))
+                if "R" in grippers and "remote.button.0" in action:
+                    grippers["R"].apply(bool(action["remote.button.0"]))
+
+            n += 1
+            if n % 60 == 0:
+                axes = {k: round(float(action.get(k, 0.0)), 3) for k in ("remote.lx", "remote.ly", "remote.rx", "remote.ry")}
+                logger.info("Applied %d actions | axes=%s", n, axes)
+    finally:
+        logger.info("Shutting down onboard controller...")
+        stop.set()
+        if state_sock is not None:
+            with contextlib.suppress(Exception):
+                state_sock.close(linger=0)
+        if camera_server is not None:
+            with contextlib.suppress(Exception):
+                camera_server.stop()
+        for g in grippers.values():
+            with contextlib.suppress(Exception):
+                g.bus.disconnect()
+        robot.disconnect()
+
+
+if __name__ == "__main__":
+    main()

src/lerobot/robots/unitree_g1/run_g1_server.py

@@ -28,9 +28,11 @@ import argparse
 import base64
 import contextlib
 import json
+import re
 import threading
 import time
-from typing import Any
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
 
 import zmq
 from unitree_sdk2py.comm.motion_switcher.motion_switcher_client import MotionSwitcherClient
@@ -41,6 +43,9 @@ from unitree_sdk2py.utils.crc import CRC
 
 from lerobot.cameras.zmq.image_server import ImageServer
 
+if TYPE_CHECKING:
+    from lerobot.motors.damiao.damiao import DamiaoMotorsBus
+
 # DDS topic names follow Unitree SDK naming conventions
 # ruff: noqa: N816
 kTopicLowCommand_Debug = "rt/lowcmd"  # action to robot
@@ -51,6 +56,105 @@ LOWSTATE_PORT = 6001
 NUM_MOTORS = 35
 
 
+@dataclass
+class Gripper:
+    """A single Damiao gripper that only writes to CAN when the open/close state changes."""
+
+    name: str
+    bus: "DamiaoMotorsBus"
+    open_deg: float
+    close_deg: float
+    _last_cmd: str | None = None  # "open" | "close"
+
+    def apply(self, want_close: bool) -> None:
+        want = "close" if want_close else "open"
+        if want == self._last_cmd:
+            return
+        target = self.close_deg if want_close else self.open_deg
+        self.bus.write("Goal_Position", "gripper", target)
+        self._last_cmd = want
+        print(f"[gripper] {self.name} -> {want.upper()} ({target:.1f} deg)")
+
+
+def build_gripper(
+    name: str,
+    port: str,
+    send_id: int,
+    recv_id: int,
+    motor_type: str,
+    use_can_fd: bool,
+    open_deg: float,
+    close_deg: float,
+    kp: float,
+    kd: float,
+) -> Gripper:
+    from lerobot.motors.damiao.damiao import DamiaoMotorsBus
+    from lerobot.motors.motors_bus import Motor, MotorNormMode
+
+    motors = {
+        "gripper": Motor(
+            id=send_id,
+            model=motor_type,
+            norm_mode=MotorNormMode.DEGREES,
+            motor_type_str=motor_type,
+            recv_id=recv_id,
+        )
+    }
+    bus = DamiaoMotorsBus(port=port, motors=motors, use_can_fd=use_can_fd)
+    print(f"Connecting {name} gripper on {port} (fd={use_can_fd})...")
+    bus.connect(handshake=True)
+    bus.write("Kp", "gripper", kp)
+    bus.write("Kd", "gripper", kd)
+    bus.write("Goal_Position", "gripper", open_deg)  # start open
+    print(f"  {name}: connected, torque enabled, opened.")
+    return Gripper(name, bus, open_deg, close_deg, _last_cmd="open")
+
+
+def parse_camera_specs(spec: str, default_width: int, default_height: int) -> dict[str, dict]:
+    """Parse a multi-camera spec string into an ImageServer ``cameras`` dict.
+
+    Format: comma-separated ``name:device[:WxH[:FOURCC]]`` entries, e.g.
+    ``head_camera:6,left_wrist:0``. ``device`` may be an integer index or an explicit
+    device path (e.g. ``/dev/video6``), including stable ``by-path`` names like
+    ``/dev/v4l/by-path/platform-...:2.1:1.3-video-index0`` which survive USB
+    re-enumeration (unlike bare ``/dev/videoN`` indices). Because a by-path name
+    itself contains colons, the optional ``WxH`` and ``FOURCC`` are parsed from the
+    *right* so the device-path colons are preserved.
+    """
+    wh_re = re.compile(r"\d+x\d+", re.IGNORECASE)
+    fourcc_re = re.compile(r"[A-Za-z0-9]{4}")
+
+    cameras: dict[str, dict] = {}
+    for entry in spec.split(","):
+        entry = entry.strip()
+        if not entry:
+            continue
+        if ":" not in entry:
+            raise ValueError(f"Invalid camera spec '{entry}', expected 'name:device[:WxH[:FOURCC]]'")
+        name, rest = entry.split(":", 1)
+        name = name.strip()
+        tokens = [t.strip() for t in rest.split(":")]
+
+        fourcc = None
+        if len(tokens) >= 3 and wh_re.fullmatch(tokens[-2]) and fourcc_re.fullmatch(tokens[-1]):
+            fourcc = tokens.pop().upper()
+        width, height = default_width, default_height
+        if len(tokens) >= 2 and wh_re.fullmatch(tokens[-1]):
+            w, h = tokens.pop().lower().split("x")
+            width, height = int(w), int(h)
+
+        raw_id = ":".join(tokens).strip()
+        if not raw_id:
+            raise ValueError(f"Invalid camera spec '{entry}', missing device")
+        device_id: int | str = int(raw_id) if raw_id.lstrip("-").isdigit() else raw_id
+        if name in cameras:
+            raise ValueError(f"Duplicate camera name '{name}' in --cameras")
+        cameras[name] = {"device_id": device_id, "shape": [height, width], "fourcc": fourcc}
+    if not cameras:
+        raise ValueError("No cameras parsed from --cameras spec")
+    return cameras
+
+
 def lowstate_to_dict(msg: hg_LowState) -> dict[str, Any]:
     """Convert LowState SDK message to a JSON-serializable dictionary."""
     motor_states = []
@@ -155,7 +259,11 @@ def main() -> None:
     """Main entry point for the robot server bridge."""
     parser = argparse.ArgumentParser(description="DDS-to-ZMQ bridge server for Unitree G1")
     parser.add_argument("--camera", action="store_true", help="Also launch camera server")
-    parser.add_argument("--camera-device", type=int, default=4, help="Camera device ID (default: 4)")
+    parser.add_argument("--camera-device", default="4",
+                        help="Camera device: index or /dev/video path or by-path name (default: 4)")
+    parser.add_argument("--cameras", default=None,
+                        help="Multi-camera spec 'name:device[:WxH[:FOURCC]]', comma-separated. Overrides "
+                             "--camera-device; device may be a by-path name to survive USB re-enumeration.")
     parser.add_argument("--camera-fps", type=int, default=30, help="Camera FPS (default: 30)")
     parser.add_argument("--camera-width", type=int, default=640, help="Camera width (default: 640)")
     parser.add_argument("--camera-height", type=int, default=480, help="Camera height (default: 480)")
@@ -164,20 +272,20 @@ def main() -> None:
 
     # Optionally start camera server in background thread
     camera_thread = None
-    if args.camera:
+    if args.camera or args.cameras:
-        camera_config = {
+        if args.cameras:
-            "fps": args.camera_fps,
+            cameras = parse_camera_specs(args.cameras, args.camera_width, args.camera_height)
-            "cameras": {
+        else:
-                "head_camera": {
+            # Single camera; accept an int index or a device/by-path string.
-                    "device_id": args.camera_device,
+            dev = args.camera_device
-                    "shape": [args.camera_height, args.camera_width],
+            dev = int(dev) if str(dev).lstrip("-").isdigit() else dev
-                }
+            cameras = {"head_camera": {"device_id": dev, "shape": [args.camera_height, args.camera_width]}}
-            },
+        camera_config = {"fps": args.camera_fps, "cameras": cameras}
-        }
         camera_server = ImageServer(camera_config, port=args.camera_port)
         camera_thread = threading.Thread(target=camera_server.run, daemon=True)
         camera_thread.start()
-        print(f"Camera server started on port {args.camera_port} (device {args.camera_device})")
+        cam_summary = ", ".join(f"{n}(dev {c['device_id']})" for n, c in cameras.items())
+        print(f"Camera server started on port {args.camera_port}: {cam_summary}")
 
     # initialize DDS
     ChannelFactoryInitialize(0)

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -33,12 +33,14 @@ from ..robot import Robot
 from .config_unitree_g1 import UnitreeG1Config
 from .g1_kinematics import G1_29_ArmIK
 from .g1_utils import (
+    KEYBOARD_KEYS_FIELD,
     REMOTE_AXES,
-    REMOTE_KEYS,
     G1_29_JointArmIndex,
     G1_29_JointIndex,
     default_remote_input,
+    lowstate_to_obs,
     make_locomotion_controller,
+    obs_to_wb34_state,
 )
 
 if TYPE_CHECKING or _unitree_sdk_available:
@@ -47,8 +49,12 @@ if TYPE_CHECKING or _unitree_sdk_available:
         ChannelPublisher as _SDKChannelPublisher,
         ChannelSubscriber as _SDKChannelSubscriber,
     )
-    from unitree_sdk2py.idl.default import unitree_hg_msg_dds__LowCmd_
+    from unitree_sdk2py.idl.default import (
+        unitree_hg_msg_dds__HandCmd_ as hg_HandCmd_default,
+        unitree_hg_msg_dds__LowCmd_,
+    )
     from unitree_sdk2py.idl.unitree_hg.msg.dds_ import (
+        HandCmd_ as hg_HandCmd,
         LowCmd_ as hg_LowCmd,
         LowState_ as hg_LowState,
     )
@@ -58,6 +64,8 @@ else:
     _SDKChannelPublisher = None
     _SDKChannelSubscriber = None
     unitree_hg_msg_dds__LowCmd_ = None
+    hg_HandCmd_default = None
+    hg_HandCmd = None
     hg_LowCmd = None
     hg_LowState = None
     CRC = None
@@ -79,6 +87,14 @@ class LocomotionController(Protocol):
 kTopicLowCommand_Debug = "rt/lowcmd"
 kTopicLowState = "rt/lowstate"
 
+# Wireless-remote button byte layout, mapped to the positional button indices the
+# locomotion controllers expect. Used in onboard mode to read the physical Unitree
+# remote from lowstate (mirrors the exo teleoperator's RemoteController).
+_REMOTE_BUTTON_MAP: list[str] = [
+    "RB", "LB", "start", "back", "RT", "LT", "", "",
+    "A", "B", "X", "Y", "up", "right", "down", "left",
+]
+
 
 @dataclass
 class MotorState:
@@ -122,8 +138,10 @@ class UnitreeG1(Robot):
         # Initialize cameras config (ZMQ-based) - actual connection in connect()
         self._cameras = make_cameras_from_configs(config.cameras)
 
-        # Import channel classes based on mode
+        # Import channel classes based on mode. Simulation and onboard both talk to a
-        if config.is_simulation:
+        # real (local) DDS via the Unitree SDK; only the laptop-side bridge client uses
+        # the ZMQ socket shim.
+        if config.is_simulation or config.onboard:
             self._ChannelFactoryInitialize = _SDKChannelFactoryInitialize
             self._ChannelPublisher = _SDKChannelPublisher
             self._ChannelSubscriber = _SDKChannelSubscriber
@@ -151,19 +169,100 @@ class UnitreeG1(Robot):
         # Lower-body controller loaded dynamically
         self.controller: LocomotionController | None = make_locomotion_controller(config.controller)
 
+        # Token-driven deploy: let a SONIC controller hold a neutral token until the
+        # first real one arrives, then hold the last token between control ticks.
+        if config.sonic_token_action and hasattr(self.controller, "token_mode"):
+            self.controller.token_mode = True
+
         # Controller thread state
         self._controller_thread = None
+        # When set, the controller loop stops publishing low commands so reset() can
+        # drive the joints directly without two publishers fighting (single-publisher).
+        self._controller_paused = threading.Event()
         self._controller_action_lock = threading.Lock()
         self.controller_input = default_remote_input()
         self.controller_output = {}
 
+        # Onboard-only: parser for the physical Unitree wireless remote (read straight
+        # from local lowstate so joystick locomotion works without a laptop round-trip).
+        self._joystick = None
+
+        # Replay-camera state: keep the encoded (raw) cells per camera and decode
+        # frames lazily as the play cursor advances, with a small frame cache, so we
+        # don't materialize gigabytes of decoded RGB at construction time.
+        self._replay_raw: dict[str, list] = {}
+        self._replay_cache: dict[tuple[str, int], np.ndarray] = {}
+        self._replay_cache_cap = 8
+        self._replay_len = 0
+        self._replay_idx = 0
+        if config.replay_camera_parquet and config.replay_camera_map:
+            self._load_replay_frames()
+
+        # Token-mode state: last 64-D SONIC latent token commanded by the policy,
+        # echoed back as ``observation.state`` so a token-output VLA closes the loop
+        # on its own previous token (see ``sonic_token_action``). Seeded to zeros;
+        # the controller's startup blend eases joints in regardless.
+        self._last_token: np.ndarray | None = None
+        if config.sonic_token_action:
+            from .controllers.sonic_whole_body import TOKEN_DIM
+
+            self._last_token = np.zeros(TOKEN_DIM, dtype=np.float32)
+
+    def _load_replay_frames(self) -> None:
+        """Load only the mapped parquet columns (encoded frames); decode on demand."""
+        import pyarrow.parquet as pq
+
+        cols_needed = list(dict.fromkeys(self.config.replay_camera_map.values()))
+        table = pq.read_table(self.config.replay_camera_parquet, columns=cols_needed)
+        self._replay_len = table.num_rows
+        self._replay_raw = {
+            cam_name: table.column(column).to_pylist()
+            for cam_name, column in self.config.replay_camera_map.items()
+        }
+        logger.info(
+            "Loaded %d replay frames (lazy-decode) for cameras %s from %s",
+            self._replay_len,
+            list(self.config.replay_camera_map),
+            self.config.replay_camera_parquet,
+        )
+
+    def _decode_replay_cell(self, cell) -> np.ndarray:
+        import io
+
+        from PIL import Image
+
+        data = cell["bytes"] if isinstance(cell, dict) else cell
+        return np.asarray(Image.open(io.BytesIO(data)).convert("RGB"), dtype=np.uint8)
+
+    def _replay_frame(self, cam_name: str, idx: int) -> np.ndarray:
+        """Decode (and briefly cache) a single replay frame for a camera."""
+        key = (cam_name, idx)
+        cached = self._replay_cache.get(key)
+        if cached is not None:
+            return cached
+        frame = self._decode_replay_cell(self._replay_raw[cam_name][idx])
+        if len(self._replay_cache) >= self._replay_cache_cap:
+            self._replay_cache.pop(next(iter(self._replay_cache)))
+        self._replay_cache[key] = frame
+        return frame
+
     def _subscribe_lowstate(self):  # polls robot state @ 250Hz
         while not self._shutdown_event.is_set():
             start_time = time.time()
 
             # Step simulation if in simulation mode
             if self.config.is_simulation and self.sim_env is not None:
-                self.sim_env.step()
+                try:
+                    self.sim_env.step()
+                except ValueError as e:
+                    # Startup race: the sim thread can step once before reset() has
+                    # written a valid base pose, giving a zero-norm pelvis quaternion
+                    # (scipy>=1.11 raises instead of normalizing). Skip and retry so
+                    # the thread survives instead of dying and freezing the sim.
+                    if "zero norm" not in str(e).lower():
+                        raise
+                    time.sleep(self.control_dt)
+                    continue
 
             msg = self.lowstate_subscriber.Read()
             if msg is not None:
@@ -231,15 +330,80 @@ class UnitreeG1(Robot):
                 features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
         return features
 
+    @property
+    def _wb_state_ft(self) -> dict[str, type]:
+        """34-D whole-body proprio state (``wb_state.{i}.pos``) for dense controllers.
+
+        Exposed only when the controller consumes a dense whole-body command
+        (OpenHLM / pi0.5). These ``.pos`` scalars are aggregated by the rollout
+        pipeline into a single 34-D ``observation.state`` for the policy.
+        """
+        if self.config.sonic_token_action:
+            return {}
+        if not getattr(self.controller, "wb_action", False):
+            return {}
+        from .g1_utils import WB_ACTION_DIM
+
+        return {f"wb_state.{i}.pos": float for i in range(WB_ACTION_DIM)}
+
+    @property
+    def _token_state_ft(self) -> dict[str, type]:
+        """64-D SONIC latent-token proprio state (``motion_token_state.{i}.pos``).
+
+        Exposed only in ``sonic_token_action`` mode; aggregated by the rollout into a
+        64-D ``observation.state`` (the last token the policy commanded).
+        """
+        if not self.config.sonic_token_action:
+            return {}
+        from .controllers.sonic_whole_body import TOKEN_DIM, token_state_key
+
+        return {token_state_key(i): float for i in range(TOKEN_DIM)}
+
+    @property
+    def _empty_cameras_ft(self) -> dict[str, tuple]:
+        """Synthetic zero-image cameras (see ``UnitreeG1Config.empty_cameras``)."""
+        h, w = self.config.empty_camera_hw
+        return dict.fromkeys(self.config.empty_cameras, (h, w, 3))
+
+    @property
+    def _replay_cameras_ft(self) -> dict[str, tuple]:
+        """Replay cameras, shaped from their first (lazily decoded) frame."""
+        if not self._replay_len:
+            return {}
+        return {name: self._replay_frame(name, 0).shape for name in self._replay_raw}
+
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._motors_ft, **self._cameras_ft}
+        return {
+            **self._motors_ft,
+            **self._wb_state_ft,
+            **self._token_state_ft,
+            **self._empty_cameras_ft,
+            **self._replay_cameras_ft,
+            **self._cameras_ft,
+        }
 
     @cached_property
     def action_features(self) -> dict[str, type]:
         if self.controller is None:
             return {f"{G1_29_JointIndex(motor).name}.q": float for motor in G1_29_JointIndex}
 
+        # Token-output VLA (SONIC decoder): advertise a 64-D latent-token action space
+        # (``motion_token.{i}.pos``) so ``lerobot-rollout`` maps a 64-D policy output
+        # straight onto the decoder, bypassing the encoder.
+        if self.config.sonic_token_action:
+            from .controllers.sonic_whole_body import TOKEN_DIM, token_action_key
+
+            return {token_action_key(i): float for i in range(TOKEN_DIM)}
+
+        # Dense whole-body controllers (SONIC / OpenHLM, pi0.5) consume a single
+        # 34-D command per tick. Expose it as ``wb.{i}.pos`` joint-position features
+        # so ``lerobot-rollout`` maps a 34-D policy output straight onto the robot.
+        if getattr(self.controller, "wb_action", False):
+            from .g1_utils import WB_ACTION_DIM, wb_action_key
+
+            return {wb_action_key(i): float for i in range(WB_ACTION_DIM)}
+
         arm_features = {f"{G1_29_JointArmIndex(motor).name}.q": float for motor in G1_29_JointArmIndex}
         remote_features = dict.fromkeys(REMOTE_AXES, float)
         return {**arm_features, **remote_features}
@@ -255,6 +419,11 @@ class UnitreeG1(Robot):
         while not self._shutdown_event.is_set():
             start_time = time.time()
 
+            # Paused during reset() so the reset routine is the sole low-cmd publisher.
+            if self._controller_paused.is_set():
+                time.sleep(control_dt)
+                continue
+
             with self._lowstate_lock:
                 lowstate = self._lowstate
 
@@ -271,6 +440,13 @@ class UnitreeG1(Robot):
                 with self._controller_action_lock:
                     controller_input = dict(self.controller_input)
 
+                # Onboard: the physical Unitree remote (in local lowstate) takes
+                # priority for locomotion when active; otherwise laptop/ZMQ axes stand.
+                if self.config.onboard:
+                    wl = self._wireless_remote_input(lowstate)
+                    if wl is not None:
+                        controller_input.update(wl)
+
                 # Run controller step
                 controller_action = self.controller.run_step(controller_input, lowstate)
 
@@ -293,15 +469,105 @@ class UnitreeG1(Robot):
     def configure(self) -> None:
         pass
 
+    def _wireless_remote_input(self, lowstate) -> dict | None:
+        """Parse the physical Unitree remote from lowstate into controller inputs.
+
+        Onboard only. Returns None when the remote is idle so the laptop-provided
+        (ZMQ) axes keep control; otherwise the physical remote takes priority.
+        """
+        js = self._joystick
+        if js is None:
+            return None
+        wr = getattr(lowstate, "wireless_remote", None)
+        if not wr or len(wr) < 24:
+            return None
+        try:
+            js.extract(wr)
+        except Exception:  # noqa: BLE001
+            return None
+
+        axes = {
+            "remote.lx": float(js.lx.data),
+            "remote.ly": float(js.ly.data),
+            "remote.rx": float(js.rx.data),
+            "remote.ry": float(js.ry.data),
+        }
+        active = any(abs(v) > 1e-2 for v in axes.values())
+        out = dict(axes)
+        for i, name in enumerate(_REMOTE_BUTTON_MAP):
+            if name:
+                val = float(getattr(js, name).data)
+                out[f"remote.button.{i}"] = val
+                if val:
+                    active = True
+        return out if active else None
+
+    def _release_motion_control(self) -> None:
+        """Release the robot's built-in motion services so we can send raw lowcmd.
+
+        Onboard-only. Mirrors run_g1_server.py: on the real robot the factory
+        locomotion/hand services must relinquish control before our controller can
+        write to ``rt/lowcmd``, otherwise commands are ignored or fought.
+        """
+        from unitree_sdk2py.comm.motion_switcher.motion_switcher_client import MotionSwitcherClient
+
+        msc = MotionSwitcherClient()
+        msc.SetTimeout(5.0)
+        msc.Init()
+        _, result = msc.CheckMode()
+        while result is not None and "name" in result and result["name"]:
+            logger.info("[UnitreeG1] Releasing built-in mode '%s'...", result["name"])
+            msc.ReleaseMode()
+            _, result = msc.CheckMode()
+            time.sleep(1.0)
+
     def connect(self, calibrate: bool = True) -> None:  # connect to DDS
         # Initialize DDS channel and simulation environment
         if self.config.is_simulation:
-            from lerobot.envs import make_env
+            from lerobot.envs.utils import (
+                _download_hub_file,
+                _import_hub_module,
+                _normalize_hub_result,
+            )
 
             self._ChannelFactoryInitialize(0, "lo")
-            self._env_wrapper = make_env("lerobot/unitree-g1-mujoco", trust_remote_code=True)
+            # Call the hub env's make_env directly so we can disable the offscreen
+            # head_camera renderer. We drive image-conditioned policies from recorded
+            # frames (see replay_camera_parquet / external obs), never the sim's own
+            # camera, so building a MuJoCo offscreen GL context is pure liability: it
+            # crashes with "Failed to make the EGL context current" when GLFW/SDL
+            # already own a context, killing the sim thread and hanging on
+            # "Waiting for robot state...". publish_images=False -> no renderer.
+            repo_id, _, local_file, _ = _download_hub_file(
+                "lerobot/unitree-g1-mujoco", True, None
+            )
+            hub_mod = _import_hub_module(local_file, repo_id)
+            raw = hub_mod.make_env(n_envs=1, use_async_envs=False, publish_images=False, cameras=[])
+            self._env_wrapper = _normalize_hub_result(raw)
             # Extract the actual gym env from the dict structure
             self.sim_env = self._env_wrapper["hub_env"][0].envs[0]
+        elif self.config.onboard:
+            # Real robot, controller running onboard against local DDS. Initialize the
+            # real SDK channel factory on the robot's DDS interface and take low-level
+            # control from the built-in services before we start writing lowcmd.
+            if self.config.dds_interface:
+                self._ChannelFactoryInitialize(0, self.config.dds_interface)
+            else:
+                self._ChannelFactoryInitialize(0)
+            # Real robot: hand low-level control over from the built-in services.
+            # A DDS sim has no MotionSwitcher, so this is skipped there.
+            if self.config.release_motion_control:
+                self._release_motion_control()
+            # Real robot: read the physical wireless remote from lowstate for
+            # locomotion. A sim has no physical remote, so leave _joystick=None and
+            # let send_action (ZMQ) drive the locomotion axes instead.
+            if self.config.physical_remote:
+                from unitree_sdk2py.utils.joystick import Joystick
+
+                self._joystick = Joystick()
+                for axis in (self._joystick.lx, self._joystick.ly, self._joystick.rx, self._joystick.ry):
+                    axis.smooth = 1.0
+                    axis.deadzone = 0.0
         else:
             self._ChannelFactoryInitialize(0, config=self.config)
 
@@ -311,6 +577,17 @@ class UnitreeG1(Robot):
         self.lowstate_subscriber = self._ChannelSubscriber(kTopicLowState, hg_LowState)
         self.lowstate_subscriber.Init()
 
+        # Dex3 hand command publishers (grasping). Driven by the OpenHLM grip scalars.
+        self._hand_publishers = {}
+        if self.config.publish_hands:
+            self._left_hand_cmd = hg_HandCmd_default()
+            self._right_hand_cmd = hg_HandCmd_default()
+            self._hand_publishers["left"] = self._ChannelPublisher("rt/dex3/left/cmd", hg_HandCmd)
+            self._hand_publishers["right"] = self._ChannelPublisher("rt/dex3/right/cmd", hg_HandCmd)
+            for pub in self._hand_publishers.values():
+                pub.Init()
+            logger.info("Dex3 hand command publishers initialized (rt/dex3/{left,right}/cmd)")
+
         # Start subscribe thread to read robot state
         self.subscribe_thread = threading.Thread(target=self._subscribe_lowstate)
         self.subscribe_thread.start()
@@ -343,6 +620,9 @@ class UnitreeG1(Robot):
 
         self.kp = np.array(self.config.kp, dtype=np.float32)
         self.kd = np.array(self.config.kd, dtype=np.float32)
+        if self.controller is not None and hasattr(self.controller, "kp"):
+            self.kp = np.array(self.controller.kp, dtype=np.float32)
+            self.kd = np.array(self.controller.kd, dtype=np.float32)
 
         for joint in G1_29_JointIndex:
             self.msg.motor_cmd[joint].mode = 1
@@ -350,12 +630,16 @@ class UnitreeG1(Robot):
             self.msg.motor_cmd[joint].kd = self.kd[joint.value]
             self.msg.motor_cmd[joint].q = lowstate.motor_state[joint.value].q
 
-        # Start controller thread if enabled
+        # Start controller thread if enabled. Skipped when run_controller_thread is
-        if self.controller is not None:
+        # False so a caller can step the controller synchronously (faithful replay).
+        if self.controller is not None and self.config.run_controller_thread:
             self._controller_thread = threading.Thread(target=self._controller_loop, daemon=True)
             self._controller_thread.start()
             fps = int(1.0 / self.controller.control_dt)
             logger.info(f"Controller thread started ({fps}Hz)")
+        elif self.controller is not None:
+            logger.info("Controller thread disabled (run_controller_thread=False); "
+                        "caller must drive controller.run_step synchronously.")
 
     def _send_zero_torque(self) -> None:
         """Send a zero-gain command to make joints passive before shutting down."""
@@ -371,13 +655,59 @@ class UnitreeG1(Robot):
         except Exception as e:
             logger.warning(f"Failed to send zero-torque on disconnect: {e}")
 
-    def disconnect(self):
+    def _graceful_stop(self) -> None:
-        # Put robot in passive mode before stopping threads
+        """Soft shutdown: hold the current pose and ramp joint stiffness (kp) to zero
-        if not self.config.is_simulation:
+        over ``graceful_stop_s`` while keeping damping (kd), then go passive.
-            self._send_zero_torque()
 
-        # Signal thread to stop and unblock any waits
+        Prevents the robot from collapsing the instant control ends (a bare
+        zero-torque command is kp=kd=0 ≈ free-fall). Must run after the controller
+        loop has stopped so the two aren't publishing at once.
+        """
+        if self.config.graceful_stop_s <= 0:
+            self._send_zero_torque()
+            return
+        with self._lowstate_lock:
+            lowstate = self._lowstate
+        if lowstate is None:
+            self._send_zero_torque()
+            return
+        q_hold = {f"{motor.name}.q": lowstate.motor_state[motor.value].q for motor in G1_29_JointIndex}
+        kp = np.array(self.kp, dtype=np.float32)
+        kd = np.array(self.kd, dtype=np.float32)
+        zeros = np.zeros(29, dtype=np.float32)
+        dt = self.controller.control_dt if self.controller is not None else self.config.control_dt
+        steps = max(1, int(self.config.graceful_stop_s / dt))
+        logger.info("Graceful stop: damping down over %.1fs", self.config.graceful_stop_s)
+        for i in range(steps):
+            ratio = (i + 1) / steps
+            self.publish_lowcmd(q_hold, kp=kp * (1.0 - ratio), kd=kd, tau=zeros)
+            time.sleep(dt)
+        self._send_zero_torque()
+
+    def disconnect(self):
+        # Stop the controller loop first so it isn't fighting the shutdown ramp.
         self._shutdown_event.set()
+        controller_stopped = True
+        if self._controller_thread is not None:
+            # Wait long enough for any in-flight inference tick to finish and the loop
+            # to observe the shutdown flag, so no stray low command is published while
+            # the ramp runs (the shutdown routine must be the single publisher).
+            self._controller_thread.join(timeout=5.0)
+            if self._controller_thread.is_alive():
+                controller_stopped = False
+                logger.error(
+                    "Controller thread did not stop; skipping graceful ramp to avoid "
+                    "concurrent low commands (fail-safe: joints keep last command until exit)"
+                )
+
+        # Soft, damped settle instead of an instant limp (real robot only; the
+        # subscribe thread is still alive here to supply the current pose). Only ramp
+        # once the controller thread has definitely exited.
+        if not self.config.is_simulation and controller_stopped:
+            self._graceful_stop()
+
+        if self.controller is not None and hasattr(self.controller, "shutdown"):
+            self.controller.shutdown()
 
         # Wait for subscribe thread to finish
         if self.subscribe_thread is not None:
@@ -385,12 +715,6 @@ class UnitreeG1(Robot):
             if self.subscribe_thread.is_alive():
                 logger.warning("Subscribe thread did not stop cleanly")
 
-        # Wait for controller thread to finish
-        if self._controller_thread is not None:
-            self._controller_thread.join(timeout=2.0)
-            if self._controller_thread.is_alive():
-                logger.warning("Controller thread did not stop cleanly")
-
         # Close simulation environment
         if self.config.is_simulation and self.sim_env is not None:
             try:
@@ -422,44 +746,41 @@ class UnitreeG1(Robot):
         if lowstate is None:
             return {}
 
-        obs = {}
+        # Motors + IMU + wireless remote (shared lowstate -> obs mapping)
+        obs = lowstate_to_obs(lowstate)
 
-        # Motors - q, dq, tau for all joints
+        # Dense whole-body controllers (OpenHLM / pi0.5): expose the 34-D proprio
-        for motor in G1_29_JointIndex:
+        # state as ``wb_state.{i}.pos`` so the rollout aggregates it into
-            name = motor.name
+        # ``observation.state`` for the policy.
-            idx = motor.value
+        if self.config.sonic_token_action:
-            obs[f"{name}.q"] = lowstate.motor_state[idx].q
+            # Token mode: echo the last commanded latent token as observation.state
-            obs[f"{name}.dq"] = lowstate.motor_state[idx].dq
+            # so a token-output VLA closes the loop on its own previous token.
-            obs[f"{name}.tau"] = lowstate.motor_state[idx].tau_est
+            from .controllers.sonic_whole_body import token_state_key
 
-        # IMU - gyroscope
+            token = self._last_token if self._last_token is not None else []
-        if lowstate.imu_state.gyroscope:
+            for i, v in enumerate(token):
-            obs["imu.gyro.x"] = lowstate.imu_state.gyroscope[0]
+                obs[token_state_key(i)] = float(v)
-            obs["imu.gyro.y"] = lowstate.imu_state.gyroscope[1]
+        elif getattr(self.controller, "wb_action", False):
-            obs["imu.gyro.z"] = lowstate.imu_state.gyroscope[2]
+            wb_state = obs_to_wb34_state(obs)
+            for i, v in enumerate(wb_state):
+                obs[f"wb_state.{i}.pos"] = float(v)
 
-        # IMU - accelerometer
+        # Synthetic empty cameras: black frames so image-conditioned policies run
-        if lowstate.imu_state.accelerometer:
+        # before real cameras are wired.
-            obs["imu.accel.x"] = lowstate.imu_state.accelerometer[0]
+        if self.config.empty_cameras:
-            obs["imu.accel.y"] = lowstate.imu_state.accelerometer[1]
+            h, w = self.config.empty_camera_hw
-            obs["imu.accel.z"] = lowstate.imu_state.accelerometer[2]
+            black = np.zeros((h, w, 3), dtype=np.uint8)
+            for name in self.config.empty_cameras:
+                obs[name] = black
 
-        # IMU - quaternion
+        # Replay cameras: serve the current recorded frame per camera, then advance.
-        if lowstate.imu_state.quaternion:
+        if self._replay_len:
-            obs["imu.quat.w"] = lowstate.imu_state.quaternion[0]
+            idx = self._replay_idx
-            obs["imu.quat.x"] = lowstate.imu_state.quaternion[1]
+            if idx >= self._replay_len:
-            obs["imu.quat.y"] = lowstate.imu_state.quaternion[2]
+                idx = self._replay_len - 1 if not self.config.replay_camera_loop else idx % self._replay_len
-            obs["imu.quat.z"] = lowstate.imu_state.quaternion[3]
+            for name in self._replay_raw:
-
+                obs[name] = self._replay_frame(name, idx)
-        # IMU - rpy
+            self._replay_idx += 1
-        if lowstate.imu_state.rpy:
-            obs["imu.rpy.roll"] = lowstate.imu_state.rpy[0]
-            obs["imu.rpy.pitch"] = lowstate.imu_state.rpy[1]
-            obs["imu.rpy.yaw"] = lowstate.imu_state.rpy[2]
-
-        # Wireless remote (raw bytes for teleoperator)
-        if lowstate.wireless_remote:
-            obs["wireless_remote"] = lowstate.wireless_remote
 
         # Cameras - read images from ZMQ cameras
         for cam_name, cam in self._cameras.items():
@@ -473,9 +794,19 @@ class UnitreeG1(Robot):
     def send_action(self, action: RobotAction) -> RobotAction:
         action_to_publish = action
         if self.controller is not None:
+            if self.config.sonic_token_action:
+                from .controllers.sonic_whole_body import _extract_token_from_action
+
+                token = _extract_token_from_action(action)
+                if token is not None:
+                    self._last_token = token
+            self._update_controller_action(action)
+            if self.config.publish_hands and getattr(self.controller, "wb_action", False):
+                self._publish_hand_cmds(action)
+            if getattr(self.controller, "full_body", False):
+                return action
             # Controller thread owns legs/waist. Here we only update joystick inputs
             # and publish arm targets from the teleoperator.
-            self._update_controller_action(action)
             arm_prefixes = tuple(j.name for j in G1_29_JointArmIndex)
             action_to_publish = {
                 key: value
@@ -503,11 +834,67 @@ class UnitreeG1(Robot):
         return action
 
     def _update_controller_action(self, action: RobotAction) -> None:
-        """Update controller input state from incoming teleop action."""
+        """Update controller input state from an incoming teleop action.
+
+        Controller-agnostic: every value-carrying key is forwarded verbatim into
+        ``controller_input`` (whole-body ``wb.{i}.pos`` from a 34-D VLA, or whatever a
+        future controller expects), and each controller extracts only the keys it
+        understands. The robot deliberately does not enumerate any controller's key
+        schema here.
+
+        KeyboardTeleop is the one special case: it emits the currently-pressed keys as
+        bare action keys with a ``None`` value (``dict.fromkeys(pressed, None)``), so
+        those are collected into a single held-key set under ``KEYBOARD_KEYS_FIELD``,
+        rebuilt each tick so releases clear. Special keys arrive as pynput objects and
+        are normalised to their name ("space", ...).
+        """
         with self._controller_action_lock:
-            for key in REMOTE_KEYS:
+            self.controller_input[KEYBOARD_KEYS_FIELD] = {
-                if key in action:
+                (k if isinstance(k, str) else getattr(k, "name", str(k)))
-                    self.controller_input[key] = action[key]
+                for k, value in action.items()
+                if value is None
+            }
+            for key, value in action.items():
+                if isinstance(key, str) and value is not None:
+                    self.controller_input[key] = value
+
+    def _publish_hand_cmds(self, action: RobotAction) -> None:
+        """Drive the Dex3 hands from the OpenHLM grip scalars in a 34-D wb action.
+
+        ``wb.7.pos`` is the left grip and ``wb.15.pos`` the right grip. Each scalar in
+        [0, 1] (``hand_open_grip_value`` == fully open) is turned into a curl amount and
+        scaled onto ``hand_closed_pose`` (7 joints), then published as a PD target on
+        ``rt/dex3/{left,right}/cmd`` so the fingers close when the policy grips.
+        """
+        if not self._hand_publishers:
+            return
+        from .g1_utils import wb_action_key
+
+        open_val = float(self.config.hand_open_grip_value)
+        closed_val = float(self.config.hand_closed_grip_value)
+        closed_pose = self.config.hand_closed_pose
+        kp, kd = float(self.config.hand_kp), float(self.config.hand_kd)
+        span = (closed_val - open_val) or 1.0
+
+        def curl_amount(grip: float) -> float:
+            # Fraction of the way from the open scalar to the closed scalar, in [0, 1].
+            return float(min(max((grip - open_val) / span, 0.0), 1.0))
+
+        for side, grip_idx, cmd in (
+            ("left", 7, self._left_hand_cmd),
+            ("right", 15, self._right_hand_cmd),
+        ):
+            grip = action.get(wb_action_key(grip_idx))
+            if grip is None:
+                continue
+            amount = curl_amount(float(grip))
+            for i, closed_q in enumerate(closed_pose):
+                cmd.motor_cmd[i].q = float(closed_q) * amount
+                cmd.motor_cmd[i].dq = 0.0
+                cmd.motor_cmd[i].kp = kp
+                cmd.motor_cmd[i].kd = kd
+                cmd.motor_cmd[i].tau = 0.0
+            self._hand_publishers[side].Write(cmd)
 
     @property
     def is_calibrated(self) -> bool:
@@ -537,43 +924,64 @@ class UnitreeG1(Robot):
         if default_positions is None:
             default_positions = np.array(self.config.default_positions, dtype=np.float32)
 
-        if self.config.is_simulation and self.sim_env is not None:
+        # Full-body controllers (SONIC / OpenHLM) own the whole 29-DoF command and
-            self.sim_env.reset()
+        # ignore ``<joint>.q`` in send_action(), so reset() must publish the default
-            self.publish_lowcmd(
+        # pose directly. Pause the background controller first so the two aren't both
-                {f"{motor.name}.q": float(default_positions[motor.value]) for motor in G1_29_JointIndex}
+        # writing low commands while the robot moves to the default pose.
-            )
+        full_body = getattr(self.controller, "full_body", False)
-        else:
+        paused = False
-            total_time = 3.0
+        if full_body and self._controller_thread is not None:
-            num_steps = int(total_time / control_dt)
+            self._controller_paused.set()
+            paused = True
+            time.sleep(control_dt)  # let any in-flight controller tick settle
 
-            # get current state
+        try:
-            obs = self.get_observation()
+            if self.config.is_simulation and self.sim_env is not None:
+                self.sim_env.reset()
+                self.publish_lowcmd(
+                    {f"{motor.name}.q": float(default_positions[motor.value]) for motor in G1_29_JointIndex}
+                )
+            else:
+                total_time = 3.0
+                num_steps = int(total_time / control_dt)
 
-            # record current positions
+                # get current state
-            init_dof_pos = np.zeros(29, dtype=np.float32)
+                obs = self.get_observation()
-            for motor in G1_29_JointIndex:
-                init_dof_pos[motor.value] = obs[f"{motor.name}.q"]
 
-            # Interpolate to default position
+                # record current positions
-            for step in range(num_steps):
+                init_dof_pos = np.zeros(29, dtype=np.float32)
-                start_time = time.time()
-
-                alpha = step / num_steps
-                action_dict = {}
                 for motor in G1_29_JointIndex:
-                    target_pos = default_positions[motor.value]
+                    init_dof_pos[motor.value] = obs[f"{motor.name}.q"]
-                    interp_pos = init_dof_pos[motor.value] * (1 - alpha) + target_pos * alpha
-                    action_dict[f"{motor.name}.q"] = float(interp_pos)
 
-                self.send_action(action_dict)
+                # Interpolate to default position
+                for step in range(num_steps):
+                    start_time = time.time()
 
-                # Maintain constant control rate
+                    alpha = step / num_steps
-                elapsed = time.time() - start_time
+                    action_dict = {}
-                sleep_time = max(0, control_dt - elapsed)
+                    for motor in G1_29_JointIndex:
-                time.sleep(sleep_time)
+                        target_pos = default_positions[motor.value]
+                        interp_pos = init_dof_pos[motor.value] * (1 - alpha) + target_pos * alpha
+                        action_dict[f"{motor.name}.q"] = float(interp_pos)
 
-        # Reset controller internal state (gait phase, obs history, etc.)
+                    # Full-body controllers no-op in send_action(); publish the pose
-        if self.controller is not None and hasattr(self.controller, "reset"):
+                    # directly (arm-only controllers keep the send_action() path).
-            self.controller.reset()
+                    if full_body:
+                        self.publish_lowcmd(action_dict)
+                    else:
+                        self.send_action(action_dict)
+
+                    # Maintain constant control rate
+                    elapsed = time.time() - start_time
+                    sleep_time = max(0, control_dt - elapsed)
+                    time.sleep(sleep_time)
+
+            # Reset controller internal state (gait phase, obs history, etc.) before
+            # resuming so its buffers reflect the post-reset pose.
+            if self.controller is not None and hasattr(self.controller, "reset"):
+                self.controller.reset()
+        finally:
+            if paused:
+                self._controller_paused.clear()
 
         logger.info("Reached default position")

src/lerobot/utils/import_utils.py

@@ -60,8 +60,18 @@ def is_package_available(
                     # If the package can't be imported, it's not available
                     package_exists = False
             else:
-                # For packages other than "torch", don't attempt the fallback and set as not available
+                # The distribution may be published under a name that differs from the
-                package_exists = False
+                # import name (e.g. ``onnxruntime`` imports from ``onnxruntime-gpu`` /
+                # ``onnxruntime-silicon``). Resolve the import name to its actual
+                # distribution(s) and read the version from there before giving up.
+                try:
+                    dists = importlib.metadata.packages_distributions().get(import_name, [])
+                    if dists:
+                        package_version = importlib.metadata.version(dists[0])
+                    else:
+                        package_exists = False
+                except importlib.metadata.PackageNotFoundError:
+                    package_exists = False
         logging.debug(f"Detected {pkg_name} version: {package_version}")
     if return_version:
         return package_exists, package_version
@@ -123,6 +133,8 @@ _pyrealsense2_available = is_package_available("pyrealsense2") or is_package_ava
     "pyrealsense2-macosx", import_name="pyrealsense2"
 )
 _zmq_available = is_package_available("pyzmq", import_name="zmq")
+_onnxruntime_available = is_package_available("onnxruntime")
+_onnx_available = is_package_available("onnx")
 _hebi_available = is_package_available("hebi-py", import_name="hebi")
 _teleop_available = is_package_available("teleop")
 _placo_available = is_package_available("placo")