chore(format)

docs/source/cameras.mdx

@@ -164,8 +164,8 @@ includes the range reported by the sensor. Requesting an unsupported control als
 Omitted controls leave the sensor's existing automatic or manual setting unchanged. These options
 require `use_rgb=True`.
 
-On the RealSense D405, the color stream is provided by the Stereo Module, so changing manual
+Manual color controls require a dedicated RGB module. Cameras without one, such as the RealSense
-exposure or gain also affects the depth stream.
+D405, do not support them and raise an error at connection time.
 
 </hfoption>
 </hfoptions>

docs/source/env_processor.mdx

@@ -88,6 +88,20 @@ policy_preprocessor = NormalizerProcessorStep(stats=dataset_stats)
 
 The same policy can work with different environment processors, and the same environment processor can work with different policies:
 
+````python
+# Use SmolVLA policy with LIBERO environment
+# Use SmolVLA policy with LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+    env_cfg=libero_cfg,
+    policy_cfg=smolvla_cfg,
+)
+smolvla_preprocessor, smolvla_postprocessor = make_pre_post_processors(smolvla_cfg)
+# Or use ACT policy with the same LIBERO environment
+libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
+    env_cfg=libero_cfg,
+    policy_cfg=act_cfg,
+)
+act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
 ```python
 # Use SmolVLA policy with LIBERO environment
 libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
@@ -102,7 +116,6 @@ libero_preprocessor, libero_postprocessor = make_env_pre_post_processors(
     policy_cfg=act_cfg,
 )
 act_preprocessor, act_postprocessor = make_pre_post_processors(act_cfg)
-```
 
 ### 3. **Easier Experimentation**
 
@@ -132,7 +145,7 @@ class LiberoVelocityProcessorStep(ObservationProcessorStep):
         state = torch.cat([eef_pos, eef_axisangle, eef_vel,
                           gripper_pos, gripper_vel], dim=-1)  # 14D
         return state
-```
+````
 
 ### 4. **Cleaner Environment Code**
 

docs/source/hope_jr.mdx

@@ -211,7 +211,7 @@ Record, Replay and Train with Hope-JR is still experimental.
 
 ### Record
 
-This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data).
+This step records the dataset, which can be seen as an example [here](https://huggingface.co/datasets/nepyope/hand_record_test_with_video_data/settings).
 
 ```bash
 lerobot-record \

docs/source/integrate_hardware.mdx

@@ -18,7 +18,7 @@ If you're using Feetech or Dynamixel motors, LeRobot provides built-in bus inter
 - [`DynamixelMotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/dynamixel/dynamixel.py) – for controlling Dynamixel servos
 
 Please refer to the [`MotorsBus`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/motors/motors_bus.py) abstract class to learn about its API.
-For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so_follower/so_follower.py)
+For a good example of how it can be used, you can have a look at our own [SO101 follower implementation](https://github.com/huggingface/lerobot/blob/main/src/lerobot/robots/so_follower/so101_follower/so101_follower.py)
 
 Use these if compatible. Otherwise, you'll need to find or write a Python interface (not covered in this tutorial):
 

docs/source/lekiwi.mdx

@@ -51,7 +51,7 @@ In addition to these instructions, you need to install the Feetech SDK & ZeroMQ
 pip install -e ".[lekiwi]"
 ```
 
-Great 🤗! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base 🤖.
+Great :hugs:! You are now done installing LeRobot, and we can begin assembling the SO100/SO101 arms and the mobile base :robot:.
 Every time you now want to use LeRobot, you can go to the `~/lerobot` folder where we installed LeRobot and run one of the commands.
 
 # Step-by-Step Assembly Instructions

docs/source/pi0fast.mdx

@@ -174,7 +174,7 @@ The model takes images, text instructions, and robot state as input, and outputs
 
 ## Reproducing π₀Fast results
 
-We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40k steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
+We reproduce the results of π₀Fast on the LIBERO benchmark using the LeRobot implementation. We take the LeRobot PiFast base model [lerobot/pi0fast-base](https://huggingface.co/lerobot/pi0fast-base) and finetune for an additional 40kk steps in bfloat16, with batch size of 256 on 8 H100 GPUs using the [HuggingFace LIBERO dataset](https://huggingface.co/datasets/HuggingFaceVLA/libero).
 
 The finetuned model can be found here:
 

docs/source/smolvla.mdx

@@ -93,7 +93,7 @@ lerobot-train --help
 
 ## Evaluate the finetuned model and run it in real-time
 
-Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./il_robots#record-a-dataset).
+Similarly for when recording an episode, it is recommended that you are logged in to the HuggingFace Hub. You can follow the corresponding steps: [Record a dataset](./il_robots).
 Once you are logged in, you can run inference in your setup by doing:
 
 ```bash

src/lerobot/cameras/realsense/camera_realsense.py

@@ -365,11 +365,12 @@ class RealSenseCamera(Camera):
         return self._async_read(timeout_ms=10000, read_depth=read_depth)
 
     def _get_color_sensor(self) -> "rs.sensor":
-        """Returns the sensor that controls the color stream.
+        """Returns the dedicated "RGB Camera" sensor that controls the color stream.
 
-        Most RealSense cameras expose "RGB Camera" for color. The D405 has no
+        Manual color controls are only applied to a dedicated RGB module. Cameras
-        separate RGB module — its color stream comes from "Stereo Module".
+        without one (e.g. the D405, whose color stream comes from the shared
-        We try RGB Camera first, then fall back to Stereo Module.
+        "Stereo Module") are unsupported, so we never fall back to another sensor
+        to avoid altering the depth stream.
         """
         if self.rs_profile is None:
             raise RuntimeError(f"{self}: rs_profile must be initialized before use.")
@@ -377,12 +378,14 @@ class RealSenseCamera(Camera):
         device = self.rs_profile.get_device()
         sensors = {s.get_info(rs.camera_info.name): s for s in device.query_sensors()}
 
-        for name in ("RGB Camera", "Stereo Module"):
+        if "RGB Camera" in sensors:
-            if name in sensors:
+            return sensors["RGB Camera"]
-                return sensors[name]
 
         available = list(sensors.keys())
-        raise RuntimeError(f"{self}: no color sensor found. Available sensors: {available}")
+        raise RuntimeError(
+            f"{self}: manual color controls require a dedicated 'RGB Camera' module, which this camera does not have. ",
+            f"Available sensors: {available}.",
+        )
 
     def _set_sensor_option(self, sensor: "rs.sensor", option: "rs.option", value: float, label: str) -> None:
         """Sets a sensor option, re-raising range errors with actionable diagnostics."""

src/lerobot/robots/unitree_g1/config_unitree_g1.py

@@ -68,10 +68,6 @@ class UnitreeG1Config(RobotConfig):
     # Compensates for gravity on the unitree's arms using the arm ik solver
     gravity_compensation: bool = False
 
-    # Locomotion controller class name, e.g. "GrootLocomotionController",
+    # Lower-body controller class name, e.g. "GrootLocomotionController" or
-    # "HolosomaLocomotionController", or "SonicWholeBodyController". None disables it.
+    # "HolosomaLocomotionController". None disables it.
-    # Selecting "SonicWholeBodyController" implicitly switches the robot to the 64-D
-    # latent-token action/observation interface (``motion_token.{i}.pos`` action and a
-    # ``motion_token_state.{i}.pos`` state echo) so ``lerobot-rollout`` can drive a
-    # policy trained on SONIC motion tokens (e.g. nepyope/sonic_walk).
     controller: str | None = None

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

@@ -1,27 +0,0 @@
-#!/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 SonicWholeBodyController
-
-__all__ = [
-    "GrootLocomotionController",
-    "HolosomaLocomotionController",
-    "SonicWholeBodyController",
-]

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

@@ -1,378 +0,0 @@
-#!/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 decoder whole-body controller for the Unitree G1 (token-only).
-
-Pure-Python/ONNX re-implementation of the *decode* half of NVIDIA's SONIC deploy stack.
-The encoder is intentionally absent: a token-output VLA (e.g. ``nepyope/sonic_walk``)
-supplies the 64-D latent ``motion_token`` directly each tick, and the SONIC **decoder**
-maps ``token + recent proprioception history`` to a residual action that is scaled and
-added onto the standing pose (``default_angles``) to produce 50 Hz joint-position targets
-for the robot's PD controller.
-
-Index spaces: joints exist in two orderings — **IsaacLab** (policy/training order) and
-**MuJoCo** (deploy order). ``ISAACLAB_TO_MUJOCO`` / ``MUJOCO_TO_ISAACLAB`` (in g1_utils)
-convert between them. Quaternions are scalar-first ``(w, x, y, z)``.
-"""
-
-from __future__ import annotations
-
-import json
-import logging
-
-import numpy as np
-import onnx
-import onnxruntime as ort
-from huggingface_hub import hf_hub_download
-
-from ..g1_utils import (
-    ISAACLAB_TO_MUJOCO,
-    MUJOCO_TO_ISAACLAB,
-    G1_29_JointIndex,
-    get_gravity_orientation,
-)
-from ..unitree_g1 import lowstate_to_obs
-
-logger = logging.getLogger(__name__)
-
-# ── Constants (hardware-validated; see the NVIDIA SONIC deploy reference) ──────
-CONTROL_DT = 0.02  # 50 Hz control period (s)
-TOKEN_DIM = 64  # decoder latent size
-
-# SONIC decoder checkpoint: NVIDIA's decoder ONNX re-packaged with its deploy constants
-# (kp/kd PD gains, the standing pose default_angles, and the residual action_scale) embedded
-# in the ONNX metadata; see upload_sonic_decoder.py for provisioning. The runtime loads the
-# model *and* all of these straight from the checkpoint (the Holosoma convention), so no
-# motor-physics math happens at deploy time.
-DEFAULT_SONIC_REPO_ID = "lerobot/sonic_decoder"
-DECODER_FILENAME = "model_decoder.onnx"
-DECODER_INPUT_DIM = 994  # token(64) + 10-frame proprio history + gravity
-
-
-def load_sonic_decoder(repo_id: str = DEFAULT_SONIC_REPO_ID):
-    """Load the SONIC decoder ONNX and its baked-in deploy constants from the checkpoint.
-
-    Returns ``(decoder_session, kp, kd, default_angles, action_scale, neutral_token)``. The
-    gains/pose/scale are (29,) float32 in IsaacLab joint order and ``neutral_token`` is the
-    (64,) float32 idle latent -- all read from the ONNX ``metadata_props`` rather than
-    recomputed/hardcoded at deploy time (mirrors ``holosoma_locomotion.load_policy``).
-    """
-    decoder_path = hf_hub_download(repo_id=repo_id, filename=DECODER_FILENAME)
-    so = ort.SessionOptions()
-    so.log_severity_level = 3  # quiet ORT logs
-    session = ort.InferenceSession(decoder_path, sess_options=so)
-    dec_dim = int(session.get_inputs()[0].shape[1])
-    if dec_dim != DECODER_INPUT_DIM:
-        raise RuntimeError(f"Unexpected decoder input dim {dec_dim} (expected {DECODER_INPUT_DIM})")
-
-    meta = {p.key: p.value for p in onnx.load(decoder_path, load_external_data=False).metadata_props}
-    required = ("kp", "kd", "default_angles", "action_scale", "neutral_token")
-    missing = [k for k in required if k not in meta]
-    if missing:
-        raise ValueError(
-            f"SONIC decoder ONNX at {repo_id} is missing metadata {missing}; "
-            "re-run upload_sonic_decoder.py to (re)provision the checkpoint."
-        )
-    arr = {k: np.array(json.loads(meta[k]), dtype=np.float32) for k in required}
-    logger.info("Loaded SONIC deploy constants from %s (%d joints)", repo_id, len(arr["kp"]))
-    return session, arr["kp"], arr["kd"], arr["default_angles"], arr["action_scale"], arr["neutral_token"]
-
-
-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 are a fixed convention validated against the deployed SONIC ONNX
-    policy (the decoder consumes vectors in this order). Do not "correct" the table or
-    rename toward the opposite direction without re-validating on hardware.
-    """
-    return a[MUJOCO_TO_ISAACLAB]
-
-
-# Action-feature prefix for the latent-token interface (see _extract_token_from_action).
-TOKEN_ACTION_PREFIX = "motion_token"  # nosec B105 - feature-key prefix, not a secret
-# 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"  # nosec B105 - feature-key prefix, not a secret
-
-
-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"
-
-
-# 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
-
-
-def _extract_token_from_action(action: dict | None) -> np.ndarray | None:
-    """Reassemble a dense (64,) latent token from ``motion_token.{i}`` keys, or None.
-
-    The token-only interface: the caller supplies the 64-D encoder latent directly (e.g. a
-    token-output VLA's action), 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)
-
-
-class SonicDecoder:
-    """Runs the SONIC decoder ONNX model and owns the proprioception history.
-
-    Each tick it appends the latest robot state to 10-frame history buffers, then maps the
-    supplied 64-D ``token`` + that history to a residual action added onto ``default_angles``.
-    The encoder is bypassed entirely (token supplied by the policy). ``default_angles`` and
-    ``action_scale`` are (29,) float32 in IsaacLab order, loaded from the checkpoint.
-    """
-
-    def __init__(self, decoder, default_angles, action_scale):
-        self.decoder = decoder
-        self.decoder_input = decoder.get_inputs()[0].name
-        self.default_angles = np.asarray(default_angles, np.float32)
-        self.action_scale = np.asarray(action_scale, np.float32)
-        self.default_angles_mj = _to_mujoco(self.default_angles)
-        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
-
-    def reset(self):
-        """Clear the token and 10-frame proprioception history.
-
-        ``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
-
-    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 - self.default_angles_mj] + 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]
-
-    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 step(self, robot_obs, token, debug=False):
-        """One control tick: read robot obs, decode the supplied token -> joint targets.
-
-        Args:
-            robot_obs: dict with ``<joint>.q``/``.dq`` and ``imu.*`` fields.
-            token: 64-D latent supplied by the policy (encoder bypassed).
-            debug: log action/delta norms.
-
-        Returns:
-            dict of ``<joint>.q`` target positions (rad) in IsaacLab joint order.
-        """
-        self.token = np.asarray(token, np.float32)
-        jnames = [m.name for m in G1_29_JointIndex]
-        q = np.array(
-            [
-                robot_obs.get(f"{n}.q", self.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)
-        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 = self.default_angles + action_mj[ISAACLAB_TO_MUJOCO] * self.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 SonicRuntime:
-    """Loads the SONIC decoder ONNX model and owns the decode controller.
-
-    Token-only deploy: the encoder is bypassed; each tick the decoder consumes a 64-D
-    latent token supplied directly by the policy.
-    """
-
-    def __init__(self):
-        decoder_sess, self.kp, self.kd, default_angles, action_scale, neutral_token = load_sonic_decoder()
-        self.default_angles = default_angles
-        self.neutral_token = neutral_token
-        self.controller = SonicDecoder(decoder_sess, default_angles, action_scale)
-
-    @property
-    def pipeline(self):
-        return self.controller
-
-    def reset(self):
-        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
-
-    def __init__(self):
-        logger.info("Loading SONIC whole-body controller...")
-        self._runtime = SonicRuntime()
-        self.kp = self._runtime.kp
-        self.kd = self._runtime.kd
-        self.controller = self._runtime.controller
-        self._default_angles = self._runtime.default_angles
-        self._neutral_token = self._runtime.neutral_token
-
-        # 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] = {}
-
-        # Token-interface state. ``token_mode`` is set True by the robot whenever a SONIC
-        # whole-body controller is selected (token-driven deploy): the controller then holds a
-        # stable *neutral* 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_server, lerobot-rollout and the sim replays.
-        self.token_mode = False
-        self._last_token: np.ndarray | None = None
-
-        logger.info("SONIC ready (decoder, 64-D token command path)")
-
-    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", self._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 (token-output VLA): a dense 64-D ``motion_token.{i}`` command
-        # is decoded directly, encoder bypassed.
-        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 checkpoint's neutral
-            # token, which the decoder maps to a stable, natural standing pose.
-            self._last_token = self._neutral_token.copy()
-        if self._last_token is None:
-            # No token yet and not in token_mode: hold (keep last target).
-            return {}
-        # 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.controller.step(obs, self._last_token))
-
-    def reset(self):
-        self._runtime.reset()
-        self._init_step = 0  # re-run the startup blend after a reset
-        self._start_pose = {}
-        # 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

@@ -23,47 +23,6 @@ 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,
-)
-# The two orderings are inverses of each other, so derive one from the other (argsort) to
-# guarantee they can never drift out of sync.
-MUJOCO_TO_ISAACLAB = np.argsort(ISAACLAB_TO_MUJOCO).astype(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
@@ -109,9 +68,8 @@ def make_locomotion_controller(name: str | None):
     if name is None:
         return None
     controllers = {
-        "GrootLocomotionController": "lerobot.robots.unitree_g1.controllers.gr00t_locomotion",
+        "GrootLocomotionController": "lerobot.robots.unitree_g1.gr00t_locomotion",
-        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.controllers.holosoma_locomotion",
+        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.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/gr00t_locomotion.py

@@ -21,7 +21,7 @@ import numpy as np
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from ..g1_utils import (
+from .g1_utils import (
     REMOTE_AXES,
     REMOTE_BUTTONS,
     G1_29_JointIndex,

src/lerobot/robots/unitree_g1/holosoma_locomotion.py

@@ -22,7 +22,7 @@ import onnx
 import onnxruntime as ort
 from huggingface_hub import hf_hub_download
 
-from ..g1_utils import (
+from .g1_utils import (
     REMOTE_AXES,
     G1_29_JointArmIndex,
     G1_29_JointIndex,

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -34,6 +34,7 @@ from .config_unitree_g1 import UnitreeG1Config
 from .g1_kinematics import G1_29_ArmIK
 from .g1_utils import (
     REMOTE_AXES,
+    REMOTE_KEYS,
     G1_29_JointArmIndex,
     G1_29_JointIndex,
     default_remote_input,
@@ -105,47 +106,6 @@ class G1_29_LowState:  # noqa: N801
     mode_machine: int = 0  # Robot mode
 
 
-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
-
-
 class UnitreeG1(Robot):
     config_class = UnitreeG1Config
     name = "unitree_g1"
@@ -188,60 +148,22 @@ class UnitreeG1(Robot):
 
         self.arm_ik = G1_29_ArmIK() if config.gravity_compensation else None
 
-        # Lower-body / whole-body controller loaded dynamically
+        # Lower-body controller loaded dynamically
         self.controller: LocomotionController | None = make_locomotion_controller(config.controller)
 
-        # A SONIC whole-body controller always runs in token mode: it holds a neutral
-        # token until the first real one arrives, then holds the last token between ticks.
-        if self.controller is not None 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 = {}
 
-        # 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. Implicit whenever the SONIC whole-body controller
-        # is active. Seeded to zeros; the controller's startup blend eases joints in.
-        self._last_token: np.ndarray | None = None
-        if self._sonic_token:
-            from .controllers.sonic_whole_body import TOKEN_DIM
-
-            self._last_token = np.zeros(TOKEN_DIM, dtype=np.float32)
-
-    @property
-    def _sonic_token(self) -> bool:
-        """Whether the SONIC whole-body decoder is active.
-
-        A SONIC controller consumes a 64-D latent motion token as its action and echoes
-        the last commanded token as ``observation.state``. Keyed purely off the selected
-        controller so the token interface is implicit -- no separate config flag.
-        """
-        return self.config.controller == "SonicWholeBodyController"
-
     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:
-                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:
@@ -309,38 +231,15 @@ class UnitreeG1(Robot):
                 features[f"{cam}_depth"] = (cfg.height, cfg.width, 1)
         return features
 
-    @property
-    def _token_state_ft(self) -> dict[str, type]:
-        """64-D SONIC latent-token proprio state (``motion_token_state.{i}.pos``).
-
-        Exposed only when a SONIC whole-body controller is active; aggregated by the
-        rollout into a 64-D ``observation.state`` (the last token the policy commanded).
-        """
-        if not self._sonic_token:
-            return {}
-        from .controllers.sonic_whole_body import TOKEN_DIM, token_state_key
-
-        return {token_state_key(i): float for i in range(TOKEN_DIM)}
-
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
-        return {**self._motors_ft, **self._token_state_ft, **self._cameras_ft}
+        return {**self._motors_ft, **self._cameras_ft}
 
     @cached_property
     def action_features(self) -> dict[str, type]:
-        # No controller configured at all: raw 29-DoF joint teleop.
         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._sonic_token:
-            from .controllers.sonic_whole_body import TOKEN_DIM, token_action_key
-
-            return {token_action_key(i): float for i in range(TOKEN_DIM)}
-
-        # Locomotion controllers (GR00T / Holosoma): arm joint targets + joystick axes.
         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}
@@ -356,11 +255,6 @@ 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
 
@@ -449,9 +343,6 @@ 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
@@ -500,10 +391,6 @@ class UnitreeG1(Robot):
             if self._controller_thread.is_alive():
                 logger.warning("Controller thread did not stop cleanly")
 
-        # Release controller resources (e.g. SONIC decoder sessions).
-        if self.controller is not None and hasattr(self.controller, "shutdown"):
-            self.controller.shutdown()
-
         # Close simulation environment
         if self.config.is_simulation and self.sim_env is not None:
             try:
@@ -574,15 +461,6 @@ class UnitreeG1(Robot):
         if lowstate.wireless_remote:
             obs["wireless_remote"] = lowstate.wireless_remote
 
-        # Token mode: echo the last commanded latent token as observation.state so a
-        # token-output VLA closes the loop on its own previous token.
-        if self._sonic_token:
-            from .controllers.sonic_whole_body import token_state_key
-
-            token = self._last_token if self._last_token is not None else []
-            for i, v in enumerate(token):
-                obs[token_state_key(i)] = float(v)
-
         # Cameras - read images from ZMQ cameras
         for cam_name, cam in self._cameras.items():
             if getattr(cam, "use_rgb", True):
@@ -595,22 +473,9 @@ class UnitreeG1(Robot):
     def send_action(self, action: RobotAction) -> RobotAction:
         action_to_publish = action
         if self.controller is not None:
-            # SONIC decoder: pull the 64-D latent token out of the action and remember it
-            # for the observation.state echo. The controller thread reads it back from
-            # controller_input (populated below) and decodes it into a 29-DoF command.
-            if self._sonic_token:
-                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)
-            # Full-body controllers (SONIC) own the whole 29-DoF command; nothing to
-            # publish here (the controller thread is the sole publisher).
-            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
@@ -638,17 +503,11 @@ class UnitreeG1(Robot):
         return action
 
     def _update_controller_action(self, action: RobotAction) -> None:
-        """Update controller input state from an incoming teleop action.
+        """Update controller input state from incoming teleop action."""
-
-        Controller-agnostic: every value-carrying key (locomotion ``remote.*`` axes or
-        SONIC ``motion_token.*`` values) is forwarded verbatim into ``controller_input``
-        and each controller extracts only the keys it understands. The robot deliberately
-        does not enumerate any controller's key schema here.
-        """
         with self._controller_action_lock:
-            for key, value in action.items():
+            for key in REMOTE_KEYS:
-                if isinstance(key, str) and value is not None:
+                if key in action:
-                    self.controller_input[key] = value
+                    self.controller_input[key] = action[key]
 
     @property
     def is_calibrated(self) -> bool:
@@ -678,18 +537,6 @@ class UnitreeG1(Robot):
         if default_positions is None:
             default_positions = np.array(self.config.default_positions, dtype=np.float32)
 
-        # Full-body controllers (SONIC) own the whole 29-DoF command and ignore
-        # ``<joint>.q`` in send_action(), so reset() must publish the default pose
-        # directly. Pause the background controller first so the two aren't both writing
-        # low commands while the robot moves to the default pose.
-        full_body = getattr(self.controller, "full_body", False)
-        paused = False
-        if full_body and self._controller_thread is not None:
-            self._controller_paused.set()
-            paused = True
-            time.sleep(control_dt)  # let any in-flight controller tick settle
-
-        try:
         if self.config.is_simulation and self.sim_env is not None:
             self.sim_env.reset()
             self.publish_lowcmd(
@@ -718,11 +565,6 @@ class UnitreeG1(Robot):
                     interp_pos = init_dof_pos[motor.value] * (1 - alpha) + target_pos * alpha
                     action_dict[f"{motor.name}.q"] = float(interp_pos)
 
-                    # Full-body controllers no-op in send_action(); publish the pose
-                    # directly (arm-only controllers keep the send_action() path).
-                    if full_body:
-                        self.publish_lowcmd(action_dict)
-                    else:
                 self.send_action(action_dict)
 
                 # Maintain constant control rate
@@ -730,12 +572,8 @@ class UnitreeG1(Robot):
                 sleep_time = max(0, control_dt - elapsed)
                 time.sleep(sleep_time)
 
-            # Reset controller internal state (gait phase, obs history, etc.) before
+        # Reset controller internal state (gait phase, obs history, etc.)
-            # 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")

tests/cameras/test_realsense.py

@@ -322,15 +322,16 @@ def test_get_color_sensor_prefers_rgb_camera():
     assert camera._get_color_sensor() is rgb
 
 
-def test_get_color_sensor_falls_back_to_stereo_module():
+def test_get_color_sensor_raises_without_dedicated_rgb_module():
-    """D405 has no separate RGB module; color comes from Stereo Module."""
+    """D405 has no separate RGB module; we refuse to touch the shared Stereo Module."""
     config = RealSenseCameraConfig(serial_number_or_name="042")
     camera = RealSenseCamera(config)
 
     stereo = _make_mock_sensor("Stereo Module")
     _attach_mock_color_sensor(camera, stereo)
 
-    assert camera._get_color_sensor() is stereo
+    with pytest.raises(RuntimeError, match="dedicated 'RGB Camera' module"):
+        camera._get_color_sensor()
 
 
 def test_get_color_sensor_raises_with_available_sensors():

upload_sonic_decoder.py

@@ -1,230 +0,0 @@
-#!/usr/bin/env python3
-"""Provision the SONIC decoder checkpoint at ``lerobot/sonic_decoder``.
-
-Takes NVIDIA's ``nvidia/GEAR-SONIC/model_decoder.onnx``, embeds the SONIC deploy constants
-(``kp``/``kd`` PD gains, ``default_angles`` standing pose, the residual ``action_scale``, and
-the ``neutral_token`` idle latent) into the ONNX ``metadata_props`` (the convention Holosoma
-uses for its gains), and pushes the result to ``lerobot/sonic_decoder``. After this runs, the
-runtime loads the decoder *and* every one of these constants straight from the checkpoint --
-no motor-physics math at deploy time, so ``sonic_whole_body.py`` carries none of the
-armature/bandwidth machinery nor any hardcoded deploy constants.
-
-The constants here are derived once from Unitree motor physics (armature + target bandwidth).
-That derivation is intentionally kept in this one-off provisioning script (not the runtime);
-the shared/harmonic helper is a separate PR.
-
-Build only (no network/auth needed if the source ONNX is already cached):
-    python upload_sonic_decoder.py --out ./sonic_decoder
-
-Build + upload:
-    huggingface-cli login          # or export HF_TOKEN=...
-    python upload_sonic_decoder.py --upload
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import pathlib
-
-import numpy as np
-import onnx
-from huggingface_hub import hf_hub_download
-
-SRC_REPO_ID = "nvidia/GEAR-SONIC"
-SRC_FILENAME = "model_decoder.onnx"
-DST_REPO_ID = "lerobot/sonic_decoder"
-
-# ── SONIC deploy-constant derivation (provisioning-time only) ─────────────────
-# All constants are (29,) in IsaacLab joint order: legs, waist, arms.
-# kp = armature * w**2, kd = 4 * armature * w, with a x2 factor on the stiff joints
-# (ankles + waist). action_scale = 0.25 * effort / (armature * w**2) is the residual
-# scaling that maps decoder output to a joint-angle delta on top of default_angles.
-NATURAL_FREQ = 10.0 * 2.0 * np.pi
-MOTOR_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}
-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
-)
-DOUBLE_INDICES = {4, 5, 10, 11, 13, 14}  # ankles + waist
-
-# Nominal standing pose (rad), 29 joints in IsaacLab order. Decoder actions are residuals
-# added on top of this.
-DEFAULT_ANGLES = [
-    -0.312,
-    0.0,
-    0.0,
-    0.669,
-    -0.363,
-    0.0,  # left leg
-    -0.312,
-    0.0,
-    0.0,
-    0.669,
-    -0.363,
-    0.0,  # right leg
-    0.0,
-    0.0,
-    0.0,  # waist
-    0.2,
-    0.2,
-    0.0,
-    0.6,
-    0.0,
-    0.0,
-    0.0,  # left arm
-    0.2,
-    -0.2,
-    0.0,
-    0.6,
-    0.0,
-    0.0,
-    0.0,  # right arm
-]
-
-# Neutral idle token (64-D), held until the first real token arrives. Captured from the
-# encoder while the robot stood idle in sim: the encoder is an FSQ bottleneck (~5 bit/dim,
-# Div(16)), so tokens live on the 1/16 grid. We store the integer FSQ codes and rescale by
-# 1/16 -> an exact on-grid token that decodes to a stable, natural standing pose (unlike the
-# literal all-zero token, which is off-manifold and decodes to a slightly goofy stance).
-NEUTRAL_TOKEN_CODES = [
-    -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,
-]
-
-
-def compute_kp_kd() -> tuple[list[float], list[float]]:
-    """Return (kp, kd) as plain float lists, (29,) in IsaacLab joint order."""
-
-    def stiffness(k):
-        return MOTOR_ARMATURE[k] * NATURAL_FREQ**2
-
-    def damping(k):
-        return 4.0 * MOTOR_ARMATURE[k] * NATURAL_FREQ
-
-    kp = [(2 if i in DOUBLE_INDICES else 1) * stiffness(k) for i, k in enumerate(MOTOR_MODELS)]
-    kd = [(2 if i in DOUBLE_INDICES else 1) * damping(k) for i, k in enumerate(MOTOR_MODELS)]
-    return kp, kd
-
-
-def compute_action_scale() -> list[float]:
-    """Return the per-joint residual action scale, (29,) in IsaacLab joint order."""
-    return [0.25 * EFFORT[k] / (MOTOR_ARMATURE[k] * NATURAL_FREQ**2) for k in MOTOR_MODELS]
-
-
-def build(out_dir: pathlib.Path) -> pathlib.Path:
-    """Download the source decoder, embed the deploy-constant metadata, save to ``out_dir``."""
-    src = hf_hub_download(repo_id=SRC_REPO_ID, filename=SRC_FILENAME)
-    model = onnx.load(src)
-
-    kp, kd = compute_kp_kd()
-    neutral_token = [c / 16.0 for c in NEUTRAL_TOKEN_CODES]  # FSQ Div(16): codes -> on-grid token
-    meta = {prop.key: prop.value for prop in model.metadata_props}
-    meta["kp"] = json.dumps(kp)
-    meta["kd"] = json.dumps(kd)
-    meta["action_scale"] = json.dumps(compute_action_scale())
-    meta["default_angles"] = json.dumps(DEFAULT_ANGLES)
-    meta["neutral_token"] = json.dumps(neutral_token)
-    # Rewrite metadata_props with the merged dict.
-    del model.metadata_props[:]
-    for key, value in meta.items():
-        model.metadata_props.add(key=key, value=value)
-
-    out_dir.mkdir(parents=True, exist_ok=True)
-    out_path = out_dir / SRC_FILENAME
-    onnx.save(model, out_path)
-    print(f"Wrote {out_path} with kp/kd/action_scale/default_angles/neutral_token metadata.")
-    return out_path
-
-
-def upload(out_path: pathlib.Path) -> None:
-    from huggingface_hub import HfApi
-
-    api = HfApi()
-    api.create_repo(repo_id=DST_REPO_ID, repo_type="model", exist_ok=True)
-    api.upload_file(
-        path_or_fileobj=str(out_path),
-        path_in_repo=SRC_FILENAME,
-        repo_id=DST_REPO_ID,
-        repo_type="model",
-    )
-    print(f"Uploaded {out_path.name} -> {DST_REPO_ID}")
-
-
-def main() -> None:
-    p = argparse.ArgumentParser()
-    p.add_argument("--out", type=pathlib.Path, default=pathlib.Path("./sonic_decoder"))
-    p.add_argument("--upload", action="store_true", help="Push the built ONNX to the hub")
-    args = p.parse_args()
-
-    out_path = build(args.out)
-    if args.upload:
-        upload(out_path)
-
-
-if __name__ == "__main__":
-    main()