feat(eval): optionally push recorded eval datasets to the Hub

- Extract _infer_shape_from_obs() to reduce nesting in feature conversion
- Move dataset creation into rollout() using its own env.reset() observation,
  eliminating the extra reset in run_one()
- Replace deepcopy with _shallow_copy_obs() for raw observation stashing
- Support batch_size > 1: each parallel env records to its own dataset
  (single env skips the env_0/ nesting for simplicity)
- One-time warning for env_features keys missing from observations
- Pass recording_dir + env_features through the call chain instead of
  a pre-built recording_dataset object

examples/unitree_g1/motion_loader.py

@@ -1,174 +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.
-
-"""Load an SMPL motion clip and expose it in SONIC's encoder format.
-
-SONIC's whole-body tracking mode (``encode_mode == 2``) consumes a flat
-720-vector ``smpl_joints_10frame_step1`` = 10 consecutive frames x 24 SMPL
-joints x 3 (xyz) at 50 Hz.
-
-IMPORTANT - frame convention: the encoder expects each frame's joints with the
-body's *root orientation removed* (per-frame canonical), exactly like the live
-deploy stream's ``smpl_joints_local`` (see ``process_smpl_joints`` in the GEAR
-PICO teleop and ``smpl_joints_multi_future_local`` in training). The reference
-``smpl_filtered`` clips instead store **world-frame** joints (heading retained),
-so feeding them raw makes the robot move but mis-track / never face-forward.
-This loader therefore canonicalizes on load using the clip's per-frame root
-orientation (``pose_aa[:, :3]``):
-
-    A     = Rx(+90deg) * rotvec(pose_aa[:, :3])        # y-up -> z-up root quat
-    local = base120 * A^-1 * joints                    # remove root orient
-
-with ``base120 = quat(0.5,0.5,0.5,0.5)`` (SMPL base rotation). This reproduces
-the deployed transform (verified: per-frame hip-heading std -> 0).
-
-Clip is read from a numpy ``.npz``. Expected keys:
-    smpl_joints : (T, 24, 3) float32  -- world-frame joint positions, 50 fps
-    pose_aa     : (T, 72)    float32  -- SMPL axis-angle (root = [:, :3])
-    transl      : (T, 3)     float32  -- global root translation (optional)
-    fps         : scalar
-
-Example:
-    python examples/unitree_g1/motion_loader.py \
-        --motion examples/unitree_g1/motions/walk_forward.npz
-"""
-
-import argparse
-
-import numpy as np
-
-WINDOW = 10  # frames per encoder window (smpl_joints_10frame_step1)
-N_JOINTS = 24
-JOINT_DIM = 3
-SMPL_OBS_DIM = WINDOW * N_JOINTS * JOINT_DIM  # 720
-
-
-def canonicalize_smpl_joints(smpl_joints: np.ndarray, root_aa: np.ndarray) -> np.ndarray:
-    """Remove per-frame root orientation -> SONIC ``smpl_joints_local`` format.
-
-    Args:
-        smpl_joints: (T, 24, 3) world-frame (z-up) SMPL joint positions.
-        root_aa: (T, 3) SMPL global-orient axis-angle (y-up convention).
-
-    Returns:
-        (T, 24, 3) per-frame root-orientation-removed joints.
-    """
-    from scipy.spatial.transform import Rotation as R
-
-    rx90 = R.from_euler("x", 90, degrees=True)        # smpl_root_ytoz_up
-    base120 = R.from_quat([0.5, 0.5, 0.5, 0.5])       # remove_smpl_base_rot
-    a = rx90 * R.from_rotvec(root_aa)                 # z-up root quat (left-mult)
-    b_inv = base120 * a.inv()                         # inv(remove_smpl_base_rot(a))
-    return np.einsum("tij,tkj->tki", b_inv.as_matrix(), smpl_joints).astype(np.float32)
-
-
-class SmplMotion:
-    """A single SMPL clip with SONIC-format windowing."""
-
-    def __init__(self, path: str, loop: bool = True, canonicalize: bool = True):
-        data = np.load(path)
-        smpl_joints = data["smpl_joints"].astype(np.float32)  # (T, 24, 3)
-        self.pose_aa = data["pose_aa"].astype(np.float32) if "pose_aa" in data.files else None
-        self.transl = data["transl"].astype(np.float32) if "transl" in data.files else None
-        self.fps = float(data["fps"]) if "fps" in data.files else 50.0
-        self.loop = loop
-
-        if smpl_joints.ndim != 3 or smpl_joints.shape[1:] != (N_JOINTS, JOINT_DIM):
-            raise ValueError(
-                f"Expected smpl_joints (T, {N_JOINTS}, {JOINT_DIM}), got {smpl_joints.shape}"
-            )
-
-        # Reference clips store world-frame joints; the encoder wants per-frame
-        # root-orientation-removed joints. Canonicalize when we have the root pose.
-        self.canonicalized = False
-        if canonicalize and self.pose_aa is not None:
-            smpl_joints = canonicalize_smpl_joints(smpl_joints, self.pose_aa[:, :3])
-            self.canonicalized = True
-        self.smpl_joints = smpl_joints
-
-        self.num_frames = self.smpl_joints.shape[0]
-        self._cursor = 0
-
-    def window(self, start: int) -> np.ndarray:
-        """Return the 720-vector for the 10-frame window beginning at ``start``.
-
-        Frames are laid out oldest->newest, joint-major within a frame:
-        [f0_j0_xyz, f0_j1_xyz, ..., f9_j23_xyz].
-        """
-        idx = np.arange(start, start + WINDOW)
-        if self.loop:
-            idx = np.mod(idx, self.num_frames)
-        else:
-            idx = np.clip(idx, 0, self.num_frames - 1)
-        return self.smpl_joints[idx].reshape(-1).astype(np.float32)
-
-    def reset(self):
-        self._cursor = 0
-
-    def step(self) -> np.ndarray:
-        """Advance one frame and return the current 720-vector window."""
-        w = self.window(self._cursor)
-        self._cursor += 1
-        if self.loop:
-            self._cursor %= self.num_frames
-        return w
-
-    @property
-    def done(self) -> bool:
-        return (not self.loop) and (self._cursor + WINDOW >= self.num_frames)
-
-
-def main():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--motion", required=True, help="Path to motion .npz")
-    parser.add_argument("--no-loop", action="store_true")
-    parser.add_argument("--no-canon", action="store_true",
-                        help="Skip canonicalization (feed raw stored joints)")
-    args = parser.parse_args()
-
-    m = SmplMotion(args.motion, loop=not args.no_loop, canonicalize=not args.no_canon)
-    duration = m.num_frames / m.fps
-    print(f"Loaded '{args.motion}'")
-    print(f"  frames={m.num_frames} fps={m.fps:.1f} duration={duration:.1f}s")
-    print(f"  smpl_joints={m.smpl_joints.shape} canonicalized={m.canonicalized} "
-          f"pose_aa={None if m.pose_aa is None else m.pose_aa.shape} "
-          f"transl={None if m.transl is None else m.transl.shape}")
-
-    # Sanity: after canonicalization the per-frame body heading should be fixed.
-    j = m.smpl_joints
-    v = (j[:, 2, :2] - j[:, 1, :2])  # R_hip - L_hip, horizontal
-    a = np.arctan2(v[:, 1], v[:, 0])
-    rlen = np.clip(np.hypot(np.cos(a).mean(), np.sin(a).mean()), 1e-9, 1.0)
-    circ_std = np.degrees(np.sqrt(-2 * np.log(rlen)))
-    print(f"  hip-heading circ-std={circ_std:.1f} deg "
-          f"(~0 => orientation removed; large => world-frame)")
-
-    w0 = m.window(0)
-    print(f"  window(0): shape={w0.shape} (expected {SMPL_OBS_DIM}) "
-          f"min={w0.min():.3f} max={w0.max():.3f}")
-    assert w0.shape == (SMPL_OBS_DIM,), "window must be 720-dim for obs[922:1642]"
-
-    # Simulate a few control ticks.
-    print("  stepping 5 ticks:")
-    for t in range(5):
-        w = m.step()
-        print(f"    t={t} cursor={m._cursor} window_norm={np.linalg.norm(w):.2f}")
-
-    print("OK: motion loads and yields SONIC-format 720-vec windows.")
-
-
-if __name__ == "__main__":
-    main()

examples/unitree_g1/pkl_to_npz.py

@@ -1,88 +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.
-
-"""Convert a GEAR-SONIC / BONES-SEED ``smpl_filtered`` clip (.pkl) to .npz.
-
-The reference clips are zlib-compressed joblib pickles holding a dict with
-``pose_aa`` (T, 72), ``transl`` (T, 3), ``smpl_joints`` (T, 24, 3), ``fps``.
-``motion_loader.SmplMotion`` consumes the .npz form so the runtime needs no
-joblib dependency. Canonicalization (root-orientation removal) happens at load
-time in ``motion_loader``, so this converter just repackages the raw arrays.
-
-Run this in an environment that has ``joblib`` (e.g. the sonic teleop venv):
-
-    python examples/unitree_g1/pkl_to_npz.py \
-        --pkl sample_data/smpl_filtered/walk_forward_amateur_001__A001.pkl \
-        --out examples/unitree_g1/motions/walk_forward.npz
-"""
-
-import argparse
-from pathlib import Path
-
-import numpy as np
-
-
-def load_pkl(path: str) -> dict:
-    try:
-        import joblib
-
-        return joblib.load(path)
-    except Exception:
-        # joblib clips are zlib-compressed pickles; fall back to manual inflate.
-        import pickle
-        import zlib
-
-        with open(path, "rb") as f:
-            raw = f.read()
-        try:
-            raw = zlib.decompress(raw)
-        except zlib.error:
-            pass
-        return pickle.loads(raw)
-
-
-def main():
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--pkl", required=True, help="Input smpl_filtered .pkl")
-    parser.add_argument("--out", required=True, help="Output .npz path")
-    args = parser.parse_args()
-
-    d = load_pkl(args.pkl)
-    if not isinstance(d, dict) or "smpl_joints" not in d:
-        raise ValueError(f"Unexpected pkl structure; keys={list(d) if isinstance(d, dict) else type(d)}")
-
-    smpl_joints = np.asarray(d["smpl_joints"], np.float32)
-    if smpl_joints.ndim != 3 or smpl_joints.shape[1:] != (24, 3):
-        raise ValueError(f"smpl_joints must be (T,24,3), got {smpl_joints.shape}")
-
-    out = {"smpl_joints": smpl_joints, "fps": np.float32(d.get("fps", 50.0))}
-    if "pose_aa" in d:
-        out["pose_aa"] = np.asarray(d["pose_aa"], np.float32)
-    else:
-        print("[warn] no pose_aa -> loader cannot canonicalize (will feed raw)")
-    if "transl" in d:
-        out["transl"] = np.asarray(d["transl"], np.float32)
-
-    Path(args.out).parent.mkdir(parents=True, exist_ok=True)
-    np.savez_compressed(args.out, **out)
-    dur = smpl_joints.shape[0] / float(out["fps"])
-    print(f"Wrote {args.out}")
-    print(f"  frames={smpl_joints.shape[0]} fps={float(out['fps']):.1f} duration={dur:.1f}s "
-          f"keys={sorted(out)}")
-
-
-if __name__ == "__main__":
-    main()

examples/unitree_g1/sonic.py

@@ -1,255 +0,0 @@
-#!/usr/bin/env python
-"""
-SONIC planner with full mode control.
-
-Keyboard controls:
-    N / P      - next / previous motion set
-    1-8        - select mode within current set
-    WASD       - movement direction
-    Q / E      - rotate facing left / right
-    9 / 0      - decrease / increase speed
-    - / =      - decrease / increase height
-    R          - force replan
-    M          - toggle SMPL motion playback <-> locomotion (needs --motion-file)
-    Space      - emergency stop -> IDLE
-    Esc        - quit
-
-Gamepad controls (Unitree wireless controller):
-    Left stick Y  - speed (forward = fast, back = stop)
-    Left stick X  - movement direction (offset from facing)
-    Right stick X - facing direction (incremental rotation)
-    Right stick Y - height (up = tall 0.8m, down = low 0.1m)
-    Buttons       - unused (mode selection is keyboard-only)
-
-For teleop integration use --robot.controller=SonicWholeBodyController instead.
-"""
-
-import argparse
-import faulthandler
-import gc
-import sys
-import time
-
-import numpy as np
-
-from lerobot.robots.unitree_g1.config_unitree_g1 import UnitreeG1Config
-from lerobot.robots.unitree_g1.controllers.sonic_whole_body import SonicRuntime
-from lerobot.robots.unitree_g1.controllers.sonic_pipeline import (
-    CONTROL_DT,
-    DEFAULT_ANGLES,
-    LM,
-    MOTION_SETS,
-    RawKeyboard,
-    compute_kp_kd,
-    drain_keyboard,
-)
-from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
-from lerobot.robots.unitree_g1.unitree_g1 import UnitreeG1
-
-from motion_loader import SmplMotion
-
-
-def main():
-    parser = argparse.ArgumentParser(description="SONIC planner with keyboard + gamepad control")
-    parser.add_argument("--ip", type=str, default=None,
-                        help="Robot IP for real hardware (e.g. 192.168.123.164). "
-                             "Omit for simulation.")
-    parser.add_argument("--log-csv", action="store_true",
-                        help="Write /tmp/sonic_pose_log.csv (disabled by default for teleop perf)")
-    parser.add_argument("--cpu", action="store_true",
-                        help="Force CPU ONNX Runtime (skip CUDA even if onnxruntime-gpu is installed)")
-    parser.add_argument("--headless", action="store_true",
-                        help="Ignored for sim (stock UnitreeG1 uses hub MuJoCo defaults)")
-    parser.add_argument("--gamepad", action="store_true",
-                        help="Read Unitree wireless gamepad in sim (default: keyboard-only in sim)")
-    parser.add_argument("--keyboard-only", action="store_true",
-                        help="Ignore wireless gamepad (terminal keyboard only)")
-    parser.add_argument("--motion-file", type=str, default=None,
-                        help="Play an SMPL motion clip (.npz) via SONIC whole-body mode "
-                             "(encode_mode=2) instead of locomotion planning.")
-    parser.add_argument("--no-loop", action="store_true",
-                        help="With --motion-file, play once instead of looping")
-    args = parser.parse_args()
-
-    # Surface native crashes (onnxruntime / mujoco) with a real traceback, and
-    # avoid losing buffered diagnostics if the process dies mid-loop.
-    faulthandler.enable()
-    try:
-        sys.stdout.reconfigure(line_buffering=True)
-    except Exception:
-        pass
-
-    print("=" * 60)
-    print("SONIC planner - full mode control")
-    print("  N/P  cycle sets | 1-8 select mode | WASD move")
-    print("  Q/E  rotate     | 9/0 speed       | -/= height")
-    print("  R    replan     | Space IDLE       | Esc quit")
-    if args.ip:
-        print(f"  Robot IP: {args.ip}")
-    else:
-        print("  Mode: simulation")
-    print("=" * 60 + "\n")
-
-    cfg = UnitreeG1Config(controller=None)  # full-body SONIC; standalone loop owns publish
-    if args.ip:
-        cfg.is_simulation = False
-        cfg.robot_ip = args.ip
-    else:
-        cfg.is_simulation = True
-        if args.headless:
-            print("[Note] --headless ignored: sim uses stock UnitreeG1 + hub env")
-    robot = UnitreeG1(cfg)
-    robot.connect()
-    kp, kd = compute_kp_kd()
-    robot.kp = kp.copy()
-    robot.kd = kd.copy()
-
-    runtime = SonicRuntime(force_cpu=args.cpu)
-    controller = runtime.controller
-    ms = runtime.ms
-
-    motion = None
-    if args.motion_file:
-        motion = SmplMotion(args.motion_file, loop=not args.no_loop)
-        controller.smpl_motion = motion       # lets 'M' key toggle playback
-        controller.encode_mode = 2            # start in SONIC whole-body SMPL imitation
-        dur = motion.num_frames / motion.fps
-        print(f"\n[Motion] SMPL whole-body playback: {args.motion_file}")
-        print(f"  frames={motion.num_frames} fps={motion.fps:.1f} "
-              f"duration={dur:.1f}s loop={not args.no_loop} encode_mode=2")
-        print("  Press 'M' to toggle SMPL playback <-> locomotion at runtime.")
-
-    runtime.controller.print_input_diagnostics()
-
-    print(f"\nStarting: {MOTION_SETS[0][0]} (default mode: {LM(ms.mode).name})")
-    [print(f"  {i+1}: {m.name}") for i, m in enumerate(MOTION_SETS[0][1])]
-    print("\n[Ready] Click THIS terminal, then W/A/S/D to move. "
-          "1-6 change mode, 9/0 speed, Esc quit.\n", flush=True)
-
-    # Sim hub publishes wireless_remote bytes that can fight terminal WASD.
-    base_joystick = not args.keyboard_only and (args.gamepad or args.ip is not None)
-
-    with RawKeyboard() as kb:
-        try:
-            gc.disable()
-            gc_timer = 0.0
-            robot.reset(CONTROL_DT, DEFAULT_ANGLES)
-            time.sleep(1.0)
-
-            last_status = time.time() - 2.1
-            loop_t = enc_t = dec_t = obs_t = act_t = []
-            slow_n = blend_n = 0
-            stall_src = ""
-            did_blend = False
-            prev_end = time.time()
-            t_start = time.time()
-
-            log_path = "/tmp/sonic_pose_log.csv"
-            jnames = [m.name for m in G1_29_JointIndex]
-            log_ctx = open(log_path, "w") if args.log_csv else None
-            if log_ctx:
-                log_ctx.write("t,step,cursor,ts,blend,mode," +
-                              ",".join(f"q{i}" for i in range(29)) + "," +
-                              ",".join(f"ref{i}" for i in range(29)) + "," +
-                              ",".join(f"act{i}" for i in range(29)) +
-                              ",delta_max,action_norm,token_norm\n")
-
-            try:
-                while not robot._shutdown_event.is_set():
-                    t0 = time.time()
-                    if drain_keyboard(kb, ms, controller):
-                        break
-
-                    obs = robot.get_observation()
-                    t_obs = time.time()
-                    obs_t.append(1000 * (t_obs - t0))
-                    if not obs:
-                        runtime.tick({}, use_joystick=False)
-                        time.sleep(max(0.0, CONTROL_DT - (time.time() - t0)))
-                        continue
-
-                    # SMPL playback only while in whole-body mode; 'M' toggles it.
-                    motion_active = motion is not None and controller.encode_mode == 2
-                    if motion_active:
-                        controller.smpl_joints_10frame_step1 = motion.step()
-                        if motion.done:
-                            print("\n[Motion] clip finished")
-                            break
-
-                    step_before = runtime.step
-                    t_step = time.time()
-                    action = runtime.tick(obs, use_joystick=base_joystick and not motion_active)
-                    step_ms = 1000 * (time.time() - t_step)
-                    do_enc = step_before % 5 == 0
-                    (enc_t if do_enc else dec_t).append(step_ms)
-
-                    t_act = time.time()
-                    robot.send_action(action)
-                    act_t.append(1000 * (time.time() - t_act))
-
-                    if log_ctx and runtime.step % 5 == 0:
-                        t_rel = time.time() - t_start
-                        q_r = np.array([obs.get(f"{n}.q", 0) for n in jnames])
-                        a_v = np.array([action.get(f"{n}.q", 0) for n in jnames])
-                        cur, ts = controller.ref_cursor, controller.motion_timesteps
-                        q_ref = controller.motion_joint_positions[min(cur, ts - 1)] if ts > 0 else np.zeros(29)
-                        log_ctx.write(f"{t_rel:.4f},{runtime.step},{cur},{ts},{int(did_blend)},{ms.mode}," +
-                                      ",".join(f"{v:.6f}" for v in q_r) + "," +
-                                      ",".join(f"{v:.6f}" for v in q_ref) + "," +
-                                      ",".join(f"{v:.6f}" for v in a_v) + "," +
-                                      f"{np.max(np.abs(a_v - q_r)):.6f},"
-                                      f"{np.linalg.norm(a_v):.6f},"
-                                      f"{np.linalg.norm(controller.token):.6f}\n")
-                        did_blend = False
-
-                    now = time.time()
-                    loop_ms = 1000 * (now - t0)
-                    if loop_ms > 50:
-                        stall_src = (f"[STALL] {loop_ms:.0f}ms: "
-                                     f"obs={obs_t[-1]:.0f} step={step_ms:.0f} act={act_t[-1]:.0f}")
-                    if loop_ms > CONTROL_DT * 1500:
-                        slow_n += 1
-
-                    if now - last_status > 2.0:
-                        def _avg(lst):
-                            return sum(lst) / len(lst) if lst else 0
-
-                        hz = 1000 / _avg(loop_t) if _avg(loop_t) else 0
-                        print(f"\r  {ms.status_line()}  step={runtime.step} "
-                              f"ref={controller.ref_cursor}/{controller.motion_timesteps} "
-                              f"loop={_avg(loop_t):.1f}ms(max={max(loop_t, default=0):.1f}) hz={hz:.0f} "
-                              f"enc={_avg(enc_t):.1f} dec={_avg(dec_t):.1f} obs={_avg(obs_t):.1f} "
-                              f"slow={slow_n} blends={blend_n}", end="", flush=True)
-                        if stall_src:
-                            print(f"\n  {stall_src}")
-                            stall_src = ""
-                        last_status = now
-                        loop_t = enc_t = dec_t = obs_t = act_t = []
-                        slow_n = blend_n = 0
-
-                    prev_end = time.time()
-                    gc_timer += CONTROL_DT
-                    if gc_timer >= 10.0:
-                        gc.collect()
-                        gc_timer = 0.0
-                    loop_t.append(loop_ms)
-                    time.sleep(max(0.0, CONTROL_DT - (time.time() - t0)))
-            finally:
-                if log_ctx:
-                    log_ctx.close()
-
-        except KeyboardInterrupt:
-            pass
-        finally:
-            gc.enable()
-            if args.log_csv:
-                print(f"\n[Log] Saved to {log_path}")
-            runtime.shutdown()
-            print("\nStopping...")
-            if robot.is_connected:
-                robot.disconnect()
-            print("Done.")
-
-
-if __name__ == "__main__":
-    main()

src/lerobot/configs/default.py

@@ -73,8 +73,17 @@ class EvalConfig:
     # `use_async_envs` specifies whether to use asynchronous environments (multiprocessing).
     # Defaults to True; automatically downgraded to SyncVectorEnv when batch_size=1.
     use_async_envs: bool = True
+    # Whether to record eval rollouts as a LeRobot dataset on disk.
+    recording: bool = False
+    # If set, push recorded eval datasets to the Hub under this repo id (one repo per task,
+    # suffixed by task and env index). Requires recording=true.
+    recording_repo_id: str | None = None
+    # Whether the pushed recording repositories should be private.
+    recording_private: bool = False
 
     def __post_init__(self) -> None:
+        if self.recording_repo_id is not None and not self.recording:
+            raise ValueError("eval.recording_repo_id requires eval.recording=true.")
         if self.batch_size == 0:
             self.batch_size = self._auto_batch_size()
         if self.batch_size > self.n_episodes:

src/lerobot/datasets/dataset_metadata.py

@@ -15,6 +15,7 @@
 # limitations under the License.
 import contextlib
 from collections.abc import Callable
+from copy import deepcopy
 from pathlib import Path
 
 import numpy as np
@@ -709,7 +710,7 @@ class LeRobotDatasetMetadata:
 
         obj.root.mkdir(parents=True, exist_ok=False)
 
-        features = {**features, **DEFAULT_FEATURES}
+        features = {**deepcopy(features), **DEFAULT_FEATURES}
         _validate_feature_names(features)
 
         obj.tasks = None

src/lerobot/datasets/dataset_tools.py

@@ -27,6 +27,7 @@ import logging
 import shutil
 from collections.abc import Callable
 from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed
+from copy import deepcopy
 from pathlib import Path
 
 import datasets
@@ -1101,7 +1102,9 @@ def _copy_episodes_metadata_and_stats(
     if dst_meta.video_keys and src_dataset.meta.video_keys:
         for key in dst_meta.video_keys:
             if key in src_dataset.meta.features:
-                dst_meta.info.features[key]["info"] = src_dataset.meta.info.features[key].get("info", {})
+                dst_meta.info.features[key]["info"] = deepcopy(
+                    src_dataset.meta.info.features[key].get("info", {})
+                )
 
     write_info(dst_meta.info, dst_meta.root)
 

src/lerobot/robots/unitree_g1/config_unitree_g1.py

@@ -68,6 +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",
-    # "HolosomaLocomotionController", or "SonicWholeBodyController". None disables it.
+    # Lower-body controller class name, e.g. "GrootLocomotionController" or
+    # "HolosomaLocomotionController". None disables it.
     controller: str | None = None

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

@@ -1,8 +0,0 @@
-"""Unitree G1 locomotion controllers (Groot, Holosoma, SONIC)."""
-
-__all__ = [
-    "GrootLocomotionController",
-    "HolosomaLocomotionController",
-    "SonicWholeBodyController",
-    "SonicRuntime",
-]

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

@@ -1,941 +0,0 @@
-"""SONIC planner pipeline: ONNX enc/dec/planner, movement state, and input helpers."""
-
-import math
-import queue
-import select
-import struct
-import sys
-import termios
-import threading
-import time
-import tty
-from dataclasses import dataclass
-from enum import IntEnum
-
-import numpy as np
-import onnxruntime as ort
-
-from lerobot.robots.unitree_g1.g1_utils import G1_29_JointIndex
-
-# ── Constants ────────────────────────────────────────────────────────────────
-
-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)
-
-NATURAL_FREQ = 10.0 * 2.0 * np.pi
-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):
-    return 0.25 * EFFORT[k] / (ARMATURE[k] * NATURAL_FREQ**2)
-
-_J = ["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 _J], dtype=np.float32)
-
-CONTROL_DT           = 0.02
-DEFAULT_HEIGHT       = 0.788740
-TOKEN_DIM            = 64
-ENCODER_UPDATE_EVERY = 5
-DEBUG_PRINT_EVERY    = 100
-MOTION_LOOK_AHEAD_STEPS = 2
-INITIAL_RANDOM_SEED  = 1234
-MIN_TOKENS, MAX_TOKENS = 6, 16
-K = MAX_TOKENS - MIN_TOKENS + 1
-DEADZONE             = 0.05
-BLEND_FRAMES         = 8
-
-REPLAN_INTERVAL = {
-    "running": 0.1, "crawling": 0.2, "boxing": 1.0, "default": 1.0
-}
-
-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)
-
-def _to_mujoco(a):   return a[MUJOCO_TO_ISAACLAB]
-def _to_runtime(a):  r = np.zeros(29, np.float32); r[MUJOCO_TO_ISAACLAB] = a; return r
-
-DEFAULT_ANGLES_MUJOCO = _to_mujoco(DEFAULT_ANGLES)
-ENCODER_STANDING_REF  = DEFAULT_ANGLES.copy()
-
-LOWER_BODY_IL  = np.array([0,3,6,9,13,17,1,4,7,10,14,18], dtype=np.int32)
-WRIST_IL       = np.array([23,24,25,26,27,28], dtype=np.int32)
-VR_TARGET_DEF  = np.zeros(9, dtype=np.float32)
-VR_ORN_DEF     = np.array([1,0,0,0,1,0,0,0,1,0,0,0], dtype=np.float32)
-SMPL_DEF       = np.zeros(720, dtype=np.float32)
-
-# ── PD gains ─────────────────────────────────────────────────────────────────
-
-def compute_kp_kd():
-    s = lambda k: ARMATURE[k] * NATURAL_FREQ**2
-    d = lambda k: 2.0 * 2.0 * ARMATURE[k] * NATURAL_FREQ
-    _kp_keys = ["7520_22","7520_22","7520_14","7520_22","5020","5020"] * 2 + \
-               ["7520_14","5020","5020"] + \
-               ["5020","5020","5020","5020","5020","4010","4010"] * 2
-    _kd_keys = _kp_keys
-    _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(_kp_keys)], dtype=np.float32)
-    kd = np.array([2*d(k) if i in _double else d(k) for i,k in enumerate(_kd_keys)], dtype=np.float32)
-    return kp, kd
-
-
-_kp_kd = compute_kp_kd  # backward-compatible alias
-
-
-def lowstate_to_obs(lowstate) -> dict:
-    """Build a robot observation dict from Unitree lowstate."""
-    obs: dict = {}
-    for motor in G1_29_JointIndex:
-        idx = motor.value
-        obs[f"{motor.name}.q"] = float(lowstate.motor_state[idx].q)
-        obs[f"{motor.name}.dq"] = float(lowstate.motor_state[idx].dq)
-    quat = lowstate.imu_state.quaternion
-    obs["imu.quat.w"] = float(quat[0])
-    obs["imu.quat.x"] = float(quat[1])
-    obs["imu.quat.y"] = float(quat[2])
-    obs["imu.quat.z"] = float(quat[3])
-    gyro = lowstate.imu_state.gyroscope
-    obs["imu.gyro.x"] = float(gyro[0])
-    obs["imu.gyro.y"] = float(gyro[1])
-    obs["imu.gyro.z"] = float(gyro[2])
-    wr = getattr(lowstate, "wireless_remote", None)
-    if wr is not None:
-        obs["wireless_remote"] = bytes(wr) if not isinstance(wr, (bytes, bytearray)) else wr
-    return obs
-
-
-# ── Quaternion helpers ────────────────────────────────────────────────────────
-
-def quat_conj(q):
-    return np.array([q[0], -q[1], -q[2], -q[3]], dtype=np.float32)
-
-def quat_mul(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 gravity_dir(q):
-    q = q / (np.linalg.norm(q) + 1e-8)
-    qv = np.array([0, 0, 0, -1], dtype=np.float32)
-    return quat_mul(quat_mul(quat_conj(q), qv), q)[1:]
-
-def quat_to_6d(q):
-    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):
-    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):
-    a = sign * calc_heading(q) / 2.0
-    return np.array([np.cos(a), 0, 0, np.sin(a)], dtype=np.float64)
-
-heading_quat_inv = lambda q: heading_quat(q, -1.0)
-
-def quat_slerp(q0, q1, 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):
-    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)
-
-# ── Locomotion modes ──────────────────────────────────────────────────────────
-
-class LocomotionMode(IntEnum):
-    IDLE=0; SLOW_WALK=1; WALK=2; RUN=3; SQUAT=4; KNEEL_TWO_LEGS=5; KNEEL=6
-    LYING_FACE_DOWN=7; CRAWLING=8; IDLE_BOXING=9; WALK_BOXING=10
-    LEFT_PUNCH=11; RIGHT_PUNCH=12; RANDOM_PUNCH=13; ELBOW_CRAWLING=14
-    LEFT_HOOK=15; RIGHT_HOOK=16; FORWARD_JUMP=17; STEALTH_WALK=18
-    INJURED_WALK=19; LEDGE_WALKING=20; OBJECT_CARRYING=21; STEALTH_WALK_2=22
-    HAPPY_DANCE_WALK=23; ZOMBIE_WALK=24; GUN_WALK=25; SCARE_WALK=26
-
-LM = LocomotionMode
-
-MOTION_SETS = [
-    ("Standing",     [LM.SLOW_WALK, LM.WALK, LM.RUN, LM.FORWARD_JUMP, LM.STEALTH_WALK, LM.INJURED_WALK]),
-    ("Squat / Low",  [LM.SQUAT, LM.KNEEL_TWO_LEGS, LM.KNEEL, LM.CRAWLING, LM.ELBOW_CRAWLING]),
-    ("Boxing",       [LM.IDLE_BOXING, LM.WALK_BOXING, LM.LEFT_PUNCH, LM.RIGHT_PUNCH,
-                      LM.RANDOM_PUNCH, LM.LEFT_HOOK, LM.RIGHT_HOOK]),
-    ("Styled Walks", [LM.LEDGE_WALKING, LM.OBJECT_CARRYING, LM.STEALTH_WALK_2,
-                      LM.HAPPY_DANCE_WALK, LM.ZOMBIE_WALK, LM.GUN_WALK, LM.SCARE_WALK]),
-]
-
-STATIC_MODES   = {LM.IDLE, LM.SQUAT, LM.KNEEL_TWO_LEGS, LM.KNEEL, LM.LYING_FACE_DOWN, LM.IDLE_BOXING}
-STANDING_MODES = {LM.IDLE, LM.SLOW_WALK, LM.WALK, LM.RUN, LM.IDLE_BOXING, LM.WALK_BOXING,
-                  LM.LEFT_PUNCH, LM.RIGHT_PUNCH, LM.RANDOM_PUNCH, LM.LEFT_HOOK, LM.RIGHT_HOOK,
-                  LM.FORWARD_JUMP, LM.STEALTH_WALK, LM.INJURED_WALK, LM.LEDGE_WALKING,
-                  LM.OBJECT_CARRYING, LM.STEALTH_WALK_2, LM.HAPPY_DANCE_WALK,
-                  LM.ZOMBIE_WALK, LM.GUN_WALK, LM.SCARE_WALK}
-BOXING_MODES   = {LM.WALK_BOXING, LM.LEFT_PUNCH, LM.RIGHT_PUNCH,
-                  LM.RANDOM_PUNCH, LM.LEFT_HOOK, LM.RIGHT_HOOK}
-SPEED_RANGES   = {LM.SLOW_WALK:(0.2,0.8), LM.WALK:(0.8,1.5), LM.RUN:(1.5,3.0),
-                  LM.CRAWLING:(0.4,1.0),  LM.ELBOW_CRAWLING:(0.7,1.0)}
-
-def clamp_mode_params(ms):
-    m = LM(ms.mode)
-    ms.height = -1.0 if m in STANDING_MODES else max(0.1, min(0.8, ms.height if ms.height>=0 else 0.2))
-    if m in STATIC_MODES:
-        ms.speed = -1.0
-    elif m in SPEED_RANGES:
-        lo, hi = SPEED_RANGES[m]
-        ms.speed = max(lo, min(hi, ms.speed if ms.speed>=0 else lo))
-    elif m in BOXING_MODES:
-        ms.speed = max(0.7, min(1.5, ms.speed if ms.speed>=0 else 0.7))
-    else:
-        ms.speed = -1.0
-
-def replan_interval(mode):
-    m = LM(mode)
-    if m == LM.RUN: return REPLAN_INTERVAL["running"]
-    if m == LM.CRAWLING: return REPLAN_INTERVAL["crawling"]
-    if m in {LM.LEFT_PUNCH, LM.RIGHT_PUNCH, LM.RANDOM_PUNCH, LM.LEFT_HOOK, LM.RIGHT_HOOK}:
-        return REPLAN_INTERVAL["boxing"]
-    return REPLAN_INTERVAL["default"]
-
-
-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"]
-
-# ── Movement state ────────────────────────────────────────────────────────────
-
-@dataclass
-class MovementState:
-    mode: int          = LM.SLOW_WALK  # not IDLE — walking modes respond to WASD
-    speed: float       = -1.0
-    height: float      = -1.0
-    facing_angle: float = 0.0
-    movement_angle: float = 0.0
-    has_movement: bool = False
-    motion_set_idx: int = 0
-    needs_replan: bool = False
-
-    @property
-    def movement_direction(self):
-        if not self.has_movement: return (0.0, 0.0, 0.0)
-        return (math.cos(self.movement_angle), math.sin(self.movement_angle), 0.0)
-
-    @property
-    def facing_direction(self):
-        return (math.cos(self.facing_angle), math.sin(self.facing_angle), 0.0)
-
-    def status_line(self):
-        return (f"[{MOTION_SETS[self.motion_set_idx][0]}] mode={self.mode}({LM(self.mode).name}) "
-                f"spd={'default' if self.speed<0 else f'{self.speed:.1f}'} "
-                f"hgt={'default' if self.height<0 else f'{self.height:.2f}'} "
-                f"facing={math.degrees(self.facing_angle):.0f}° "
-                f"{'moving' if self.has_movement else 'still'}")
-
-
-@dataclass
-class MovementSnapshot:
-    mode: int = 0
-    speed: float = -1.0
-    height: float = -1.0
-    movement: tuple[float, float, float] = (0.0, 0.0, 0.0)
-    facing: tuple[float, float, float] = (1.0, 0.0, 0.0)
-
-
-def _snapshot_ms(ms: MovementState) -> MovementSnapshot:
-    md, fd = ms.movement_direction, ms.facing_direction
-    return MovementSnapshot(ms.mode, ms.speed, ms.height, (md[0], md[1], md[2]), (fd[0], fd[1], fd[2]))
-
-
-def should_replan_request(ms: MovementState, last: MovementSnapshot, replan_timer: float, step: int) -> bool:
-    """Match C++ G1Deploy::Planner replan triggers (g1_deploy_onnx_ref.cpp)."""
-    if step <= 0:
-        return False
-    if ms.needs_replan:
-        return True
-    md, fd = ms.movement_direction, ms.facing_direction
-    facing_changed = fd != last.facing
-    height_changed = ms.height != last.height
-    mode_changed = ms.mode != last.mode
-    speed_changed = ms.speed != last.speed
-    dir_changed = md != last.movement
-    is_static = LM(ms.mode) in STATIC_MODES
-    if mode_changed or facing_changed or height_changed:
-        return True
-    time_to_replan = replan_timer >= replan_interval(ms.mode)
-    if not is_static and (speed_changed or dir_changed or (time_to_replan and ms.speed != 0)):
-        return True
-    return False
-
-# ── Encoder / Decoder ─────────────────────────────────────────────────────────
-
-class StandingEncoderDecoder:
-    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()
-        self.set_zero_reference()
-
-    def update_history(self, q, dq, ang, quat):
-        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):
-        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):
-        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):
-        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:
-            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], ref_quat)
-                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):
-        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] = gravity_dir(q); off += 3
-        assert off == 994, f"Decoder obs mismatch: {off}"
-        return obs
-
-    def run_encoder(self):
-        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):
-        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)], 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
-            print(f"token_norm={np.linalg.norm(self.token):.4f} action_norm={np.linalg.norm(action_mj):.4f} "
-                  f"delta_max={np.max(np.abs(delta)):.4f} delta_rms={np.sqrt(np.mean(delta**2)):.4f}")
-        return {f"{m.name}.q": float(target[m.value]) for m in G1_29_JointIndex}
-
-    def print_input_diagnostics(self):
-        print("\n[Diag] Standing reference checks")
-        names = {0:"g1", 1:"teleop", 2:"smpl"}
-        print(f"  encoder mode: {self.encode_mode} ({names.get(self.encode_mode,'unknown')})")
-        print(f"  DEFAULT_ANGLES range: [{DEFAULT_ANGLES.min():+.4f}, {DEFAULT_ANGLES.max():+.4f}]")
-        print(f"  anchor_6d(identity): {self._anchor_6d(np.array([1,0,0,0],np.float32), np.array([1,0,0,0],np.float32))}")
-        print(f"  gravity(identity): {gravity_dir(np.array([1,0,0,0],np.float32))} (expect [0,0,-1])")
-        dec0 = self.build_decoder_obs()
-        print(f"  decoder q-delta max: {np.max(np.abs(dec0[94:384])):.6f}")
-        print(f"  decoder dq max:      {np.max(np.abs(dec0[384:674])):.6f}")
-
-# ── Planner motion buffer ─────────────────────────────────────────────────────
-
-class PlannerMotion:
-    def __init__(self, max_frames=1500):
-        self.timesteps         = 0
-        self.joint_positions   = np.zeros((max_frames, 29), np.float64)
-        self.joint_velocities  = np.zeros((max_frames, 29), np.float64)
-        self.body_positions    = np.zeros((max_frames, 3),  np.float64)
-        self.body_quaternions  = np.zeros((max_frames, 4),  np.float64)
-        self.body_quaternions[:, 0] = 1.0
-
-# ── Subprocess planner ────────────────────────────────────────────────────────
-
-def _resample_30_to_50(qpos, n30):
-    t50 = int(np.floor(n30 / 30.0 * 50))
-    f30 = np.arange(t50) / 50.0 * 30.0
-    f0  = np.floor(f30).astype(int)
-    f1  = np.minimum(f0+1, n30-1)
-    frac, w0 = (f30-f0).astype(np.float64), None
-    w0 = 1.0 - frac
-    jp = (w0[:,None]*qpos[f0,7:36] + frac[:,None]*qpos[f1,7:36])[:,MUJOCO_TO_ISAACLAB]
-    jv = np.zeros_like(jp)
-    if t50 >= 2: jv[:t50-1] = (jp[1:] - jp[:-1]) * 50.0; jv[-1] = jv[-2]
-    return {
-        "timesteps": t50,
-        "joint_positions":  jp,
-        "joint_velocities": jv,
-        "body_positions":   w0[:,None]*qpos[f0,:3]   + frac[:,None]*qpos[f1,:3],
-        "body_quaternions": quat_slerp_batch(qpos[f0,3:7], qpos[f1,3:7], frac),
-    }
-
-def _build_planner_inputs(ctx, ms_dict, version, seed):
-    inp = {
-        "context_mujoco_qpos": ctx.astype(np.float32).reshape(1,4,36),
-        "target_vel":          np.array([ms_dict["speed"]], np.float32),
-        "mode":                np.array([ms_dict["mode"]], np.int64),
-        "movement_direction":  np.array(ms_dict["movement_direction"], np.float32).reshape(1,3),
-        "facing_direction":    np.array(ms_dict["facing_direction"],   np.float32).reshape(1,3),
-        "random_seed":         np.array([seed], np.int64),
-    }
-    if version >= 1:
-        # TensorRT deploy: allow 9–11 prediction tokens only (indices 3–5 for MIN_TOKENS=6).
-        allowed = np.zeros((1, K), np.int64)
-        if K >= 6:
-            allowed[0, 3:6] = 1
-        inp.update({
-            "height": np.array([ms_dict["height"]], np.float32),
-            "has_specific_target":       np.array([[0]], np.int64),
-            "specific_target_positions": np.zeros((1,4,3), np.float32),
-            "specific_target_headings":  np.zeros((1,4),   np.float32),
-            "allowed_pred_num_tokens":   allowed,
-        })
-    return inp
-
-def _planner_worker(path, req_q, res_q, stop_evt, version, seed, use_gpu):
-    so = ort.SessionOptions(); so.log_severity_level = 3
-    providers = _ort_providers(force_cpu=not use_gpu)
-    sess = ort.InferenceSession(path, sess_options=so, providers=providers)
-    while not stop_evt.is_set():
-        try: ctx, gf, ms_dict = req_q.get(timeout=0.05)
-        except Exception: continue
-        try:
-            inp = _build_planner_inputs(ctx, ms_dict, version, seed)
-            t0 = time.time()
-            qpos_out, num_pred = sess.run(None, inp)
-            t_inf = time.time()
-            n = int(num_pred.flat[0])
-            qpos = qpos_out[0,:n]
-            if np.any(np.isnan(qpos)): continue
-            motion = _resample_30_to_50(qpos, n)
-            motion["gen_frame"] = gf
-            print(f"[Planner] inf={1000*(t_inf-t0):.1f}ms total={1000*(time.time()-t0):.1f}ms frames={n}", flush=True)
-            while not res_q.empty():
-                try: res_q.get_nowait()
-                except queue.Empty: break
-            res_q.put(motion)
-        except Exception as e:
-            print(f"[Planner] Error: {e}", flush=True)
-
-# ── SonicPlanner ──────────────────────────────────────────────────────────────
-
-class SonicPlanner:
-    def __init__(self, session, planner_path):
-        self.session      = session
-        self.planner_path = planner_path
-        self.gen_frame    = 0
-        self.random_seed  = INITIAL_RANDOM_SEED
-        self.version      = 1 if len(session.get_inputs()) >= 11 else 0
-        self.motion_50hz  = PlannerMotion()
-        self._snapshot    = PlannerMotion()
-        self._req_q = self._res_q = self._stop_evt = self._planner_thread = None
-        self._ctrl = None
-
-    def _build_inputs(self, ctx, ms):
-        return _build_planner_inputs(
-            ctx,
-            {"mode": ms.mode, "speed": ms.speed, "height": ms.height,
-             "movement_direction": list(ms.movement_direction),
-             "facing_direction":   list(ms.facing_direction)},
-            self.version, self.random_seed)
-
-    @staticmethod
-    def build_initial_context(joint_positions):
-        ctx = np.zeros((4, 36), np.float32)
-        jp_mj = joint_positions.astype(np.float32)[ISAACLAB_TO_MUJOCO]
-        for n in range(4):
-            ctx[n, 2] = DEFAULT_HEIGHT
-            ctx[n, 3] = 1.0
-            ctx[n, 7:36] = jp_mj
-        return ctx
-
-    def _context_from_controller(self, current_frame):
-        ctrl = self._ctrl
-        gen_frame = current_frame + MOTION_LOOK_AHEAD_STEPS
-        t_arr = gen_frame / 50.0 + np.arange(4) / 30.0
-        f50 = t_arr * 50.0
-        with ctrl.motion_lock:
-            ts = ctrl.motion_timesteps
-            if ts <= 0:
-                return self.build_initial_context(DEFAULT_ANGLES)
-            bp, bq, jp = ctrl.motion_body_pos, ctrl.motion_body_quats, ctrl.motion_joint_positions
-            f0 = np.minimum(np.floor(f50).astype(int), ts - 1)
-            f1 = np.minimum(f0 + 1, ts - 1)
-        frac = f50 - f0
-        w0 = 1.0 - frac
-        ctx = np.zeros((4, 36), np.float32)
-        ctx[:, 0:3] = w0[:, None] * bp[f0] + frac[:, None] * bp[f1]
-        ctx[:, 3:7] = quat_slerp_batch(bq[f0], bq[f1], frac)
-        ij = w0[:, None] * jp[f0] + frac[:, None] * jp[f1]
-        ctx[:, 7:36] = ij[:, ISAACLAB_TO_MUJOCO]
-        self.gen_frame = gen_frame
-        return ctx
-
-    def _load_motion_in_place(self, qpos, n30, target=None):
-        if target is None: target = self.motion_50hz
-        r = _resample_30_to_50(qpos, n30)
-        n = r["timesteps"]; target.timesteps = n
-        target.joint_positions[:n]  = r["joint_positions"]
-        target.joint_velocities[:n] = r["joint_velocities"]
-        target.body_positions[:n]   = r["body_positions"]
-        target.body_quaternions[:n] = r["body_quaternions"]
-        return target
-
-    def initialize(self, joint_positions, ms):
-        ctx = self.build_initial_context(joint_positions)
-        qpos_out, num_pred = self.session.run(None, self._build_inputs(ctx, ms))
-        n = int(num_pred.flat[0]); qpos = qpos_out[0,:n]
-        if np.any(np.isnan(qpos)): raise RuntimeError("Planner initial output contains NaN")
-        print(f"[Planner] Init: {n} frames @ 30 Hz")
-        self._load_motion_in_place(qpos, n)
-        print(f"[Planner] Resampled to {self.motion_50hz.timesteps} frames @ 50 Hz")
-        return self.motion_50hz
-
-    def request_replan(self, cursor, ms):
-        if self._req_q is None: return
-        ctx = self._context_from_controller(cursor)
-        ms_dict = {"mode": ms.mode, "speed": ms.speed, "height": ms.height,
-                   "movement_direction": list(ms.movement_direction),
-                   "facing_direction":   list(ms.facing_direction)}
-        while not self._req_q.empty():
-            try: self._req_q.get_nowait()
-            except queue.Empty: break
-        self._req_q.put((ctx, self.gen_frame, ms_dict))
-
-    def try_get_new_motion(self):
-        if self._res_q is None: return None
-        result = None
-        while not self._res_q.empty():
-            try: result = self._res_q.get_nowait()
-            except queue.Empty: break
-        if result is None: return None
-        n, gf = result["timesteps"], result["gen_frame"]
-        s = self._snapshot; s.timesteps = n
-        s.joint_positions[:n]  = result["joint_positions"]
-        s.joint_velocities[:n] = result["joint_velocities"]
-        s.body_positions[:n]   = result["body_positions"]
-        s.body_quaternions[:n] = result["body_quaternions"]
-        return s, gf
-
-    def start_subprocess(self, controller, use_gpu: bool = False):
-        """Run planner ONNX in a background thread (avoids mp spawn/fork + CUDA/MuJoCo issues)."""
-        self._ctrl = controller
-        self._req_q = queue.Queue()
-        self._res_q = queue.Queue()
-        self._stop_evt = threading.Event()
-        self._planner_thread = threading.Thread(
-            target=_planner_worker,
-            args=(self.planner_path, self._req_q, self._res_q,
-                  self._stop_evt, self.version, self.random_seed, use_gpu),
-            daemon=True,
-            name="sonic-planner",
-        )
-        self._planner_thread.start()
-        print(f"[Planner] Background thread started ({'GPU' if use_gpu else 'CPU'})")
-
-    def stop_subprocess(self):
-        if self._stop_evt:
-            self._stop_evt.set()
-        if self._planner_thread is not None:
-            self._planner_thread.join(timeout=3.0)
-            print("[Planner] Background thread stopped")
-        self._planner_thread = None
-        self._req_q = self._res_q = self._stop_evt = None
-
-# ── PlannerController ─────────────────────────────────────────────────────────
-
-class PlannerController(StandingEncoderDecoder):
-    def __init__(self, planner, encoder, decoder):
-        super().__init__(encoder, decoder)
-        self.planner = planner
-        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 load_initial_motion(self, motion):
-        with self.motion_lock:
-            n = motion.timesteps
-            self.motion_timesteps = n
-            self.motion_joint_positions[:n]  = motion.joint_positions[:n]
-            self.motion_joint_velocities[:n] = motion.joint_velocities[:n]
-            self.motion_body_quats[:n]       = motion.body_quaternions[:n]
-            self.motion_body_pos[:n]         = motion.body_positions[:n]
-            self.init_ref_quat = motion.body_quaternions[0].copy()
-            self.ref_cursor = 0; self.first_motion = True
-            self.playing = True; self.delta_heading = 0.0
-
-    def blend_new_motion(self, new_motion, gen_frame):
-        """Blend like C++ CurrentFrameAdvancement: 8-frame cross-fade, then copy tail."""
-        with self.motion_lock:
-            cur = self.ref_cursor
-            new_len = gen_frame - cur + new_motion.timesteps
-            if new_len <= 0:
-                return
-            if self.motion_timesteps == 0:
-                n = new_motion.timesteps
-                self.motion_joint_positions[:n] = new_motion.joint_positions[:n]
-                self.motion_joint_velocities[:n] = new_motion.joint_velocities[:n]
-                self.motion_body_pos[:n] = new_motion.body_positions[:n]
-                self.motion_body_quats[:n] = new_motion.body_quaternions[:n]
-                self.motion_timesteps = n
-                self.ref_cursor = 0
-                self.init_ref_quat = self.motion_body_quats[0].copy()
-                self.first_motion = False
-                return
-
-            blend_start = max(0, gen_frame - cur)
-            blend_end = min(new_len, blend_start + BLEND_FRAMES)
-
-            for f in range(blend_end):
-                f_old = min(f + cur, self.motion_timesteps - 1)
-                f_new = max(0, min(f + cur - gen_frame, new_motion.timesteps - 1))
-                w_new = min(1.0, max(0.0, (f - blend_start) / BLEND_FRAMES))
-                w_old = 1.0 - w_new
-                self.motion_joint_positions[f] = (
-                    w_old * self.motion_joint_positions[f_old]
-                    + w_new * new_motion.joint_positions[f_new]
-                )
-                self.motion_joint_velocities[f] = (
-                    w_old * self.motion_joint_velocities[f_old]
-                    + w_new * new_motion.joint_velocities[f_new]
-                )
-                self.motion_body_pos[f] = (
-                    w_old * self.motion_body_pos[f_old]
-                    + w_new * new_motion.body_positions[f_new]
-                )
-                self.motion_body_quats[f] = quat_slerp(
-                    self.motion_body_quats[f_old], new_motion.body_quaternions[f_new], w_new
-                )
-
-            for f in range(blend_end, new_len):
-                f_new = max(0, min(f + cur - gen_frame, new_motion.timesteps - 1))
-                self.motion_joint_positions[f] = new_motion.joint_positions[f_new]
-                self.motion_joint_velocities[f] = new_motion.joint_velocities[f_new]
-                self.motion_body_pos[f] = new_motion.body_positions[f_new]
-                self.motion_body_quats[f] = new_motion.body_quaternions[f_new].copy()
-
-            self.motion_timesteps = new_len
-            self.first_motion = False
-            self.ref_cursor = 0
-            self.init_ref_quat = self.motion_body_quats[0].copy()
-
-    def _heading_apply_delta(self):
-        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):
-        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):
-        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)
-                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
-            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):
-        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)
-                    self.init_ref_quat = self.motion_body_quats[rf].copy()
-                self.delta_heading = 0.0
-                self.first_motion = False
-                self.reinit_heading = False
-                print(f"[Heading] init quat: {self.heading_init_base_quat}")
-        return super().step(robot_obs, update_encoder=update_encoder, debug=debug)
-
-    def advance_cursor(self):
-        """Advance one frame per 50 Hz tick (C++ current_frame_ += 1), 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)
-
-# ── Keyboard ──────────────────────────────────────────────────────────────────
-
-class RawKeyboard:
-    def __init__(self):
-        self.fd = sys.stdin.fileno()
-        self.old = termios.tcgetattr(self.fd)
-    def __enter__(self): tty.setcbreak(self.fd); return self
-    def __exit__(self, *_): termios.tcsetattr(self.fd, termios.TCSADRAIN, self.old)
-    def get_key(self):
-        return sys.stdin.read(1) if select.select([sys.stdin],[],[],0)[0] else None
-
-
-def drain_keyboard(kb, ms, controller=None) -> bool:
-    """Process all pending terminal keys this frame (return True to quit)."""
-    quit_requested = False
-    while True:
-        key = kb.get_key()
-        if key is None:
-            break
-        if process_keyboard(key, ms, controller):
-            quit_requested = True
-    return quit_requested
-
-def process_keyboard(key, ms, controller=None):
-    if key is None: return False
-    if key == '\x1b': return True
-    if key == ' ':
-        ms.mode = LM.IDLE; ms.speed = ms.height = -1.0
-        ms.has_movement = False; ms.needs_replan = True
-        if controller: controller.playing = False; controller.reinit_heading = True
-        print("\n  >> EMERGENCY STOP -> IDLE"); return False
-    if key in ('r','R'):
-        ms.needs_replan = True; print("\n  >> Manual replan"); return False
-    if key in ('m','M'):
-        if controller is not None and getattr(controller, "smpl_motion", None) is not None:
-            if controller.encode_mode == 2:
-                controller.encode_mode = 0
-                controller.playing = True; controller.reinit_heading = True
-                ms.needs_replan = True
-                print("\n  >> Motion OFF -> locomotion (mode 0). WASD to drive.")
-            else:
-                controller.encode_mode = 2
-                controller.reinit_heading = True
-                controller.smpl_motion.reset()
-                print("\n  >> Motion ON -> SMPL playback (mode 2)")
-        else:
-            print("\n  >> No motion loaded (start with --motion-file)")
-        return False
-    if key in ('n','N','p','P'):
-        ms.motion_set_idx = (ms.motion_set_idx + (1 if key in ('n','N') else -1)) % len(MOTION_SETS)
-        name, modes = MOTION_SETS[ms.motion_set_idx]
-        print(f"\n  >> Motion set: {name}")
-        [print(f"       {i+1}: {m.name}") for i,m in enumerate(modes)]
-        return False
-    if key.isdigit() and key not in ('9','0'):
-        idx = int(key) - 1; modes = MOTION_SETS[ms.motion_set_idx][1]
-        if 0 <= idx < len(modes):
-            ms.mode = modes[idx]; ms.needs_replan = True
-            if controller: controller.playing = True; controller.reinit_heading = True
-            print(f"\n  >> Mode: {LM(ms.mode).name} ({ms.mode}) [replanning...]")
-        return False
-    if key == '9':
-        ms.speed = max(0.0, (ms.speed if ms.speed>=0 else 1.0) - 0.1)
-        print(f"\n  >> Speed: {ms.speed:.1f}"); return False
-    if key == '0':
-        ms.speed = min(5.0, (ms.speed if ms.speed>=0 else 1.0) + 0.1)
-        print(f"\n  >> Speed: {ms.speed:.1f}"); return False
-    if key == '-':
-        ms.height = max(0.2, (ms.height if ms.height>=0 else DEFAULT_HEIGHT) - 0.02)
-        print(f"\n  >> Height: {ms.height:.2f}"); return False
-    if key == '=':
-        ms.height = min(1.0, (ms.height if ms.height>=0 else DEFAULT_HEIGHT) + 0.02)
-        print(f"\n  >> Height: {ms.height:.2f}"); return False
-    if key.lower() == 'w': ms.movement_angle = ms.facing_angle
-    elif key.lower() == 's': ms.movement_angle = ms.facing_angle + math.pi
-    elif key.lower() == 'a': ms.movement_angle = ms.facing_angle + math.pi/2
-    elif key.lower() == 'd': ms.movement_angle = ms.facing_angle - math.pi/2
-    if key.lower() in ('w','s','a','d'):
-        ms.has_movement = ms.needs_replan = True
-        if controller:
-            controller.playing = True
-        print(f"\n  >> Move {key.upper()} (replanning...)")
-    elif key.lower() == 'q':
-        ms.facing_angle += 0.1
-        if controller: controller.delta_heading += 0.1
-        print(f"\n  >> Facing: {math.degrees(ms.facing_angle):.0f}°")
-    elif key.lower() == 'e':
-        ms.facing_angle -= 0.1
-        if controller: controller.delta_heading -= 0.1
-        print(f"\n  >> Facing: {math.degrees(ms.facing_angle):.0f}°")
-    return False
-
-_joy_prev_active = False
-
-
-def _parse_wireless(wr):
-    """Parse wireless_remote (bytes or int-array) into (lx, ly, rx, ry)."""
-    import struct as _st
-    if not isinstance(wr, (bytes, bytearray)):
-        wr = bytes(wr)
-    if len(wr) < 24:
-        return None
-    lx = _st.unpack("f", wr[4:8])[0]
-    rx = _st.unpack("f", wr[8:12])[0]
-    ry = _st.unpack("f", wr[12:16])[0]
-    ly = _st.unpack("f", wr[20:24])[0]
-    return lx, ly, rx, ry
-
-
-def process_joystick(obs, ms, controller=None):
-    """Joystick mirrors keyboard: left stick=WASD, right stick X=Q/E, right stick Y=height."""
-    global _joy_prev_active
-    wr = obs.get("wireless_remote")
-    if wr is None:
-        return
-    parsed = _parse_wireless(wr)
-    if parsed is None:
-        return
-    lx, ly, rx, ry = parsed
-
-    # Dead zone + negate both Y axes (bridge already flips them once)
-    lx = 0.0 if abs(lx) < DEADZONE else lx
-    ly = 0.0 if abs(ly) < DEADZONE else -ly
-    rx = 0.0 if abs(rx) < DEADZONE else rx
-    ry = 0.0 if abs(ry) < DEADZONE else -ry
-
-    left_active = abs(lx) > 0 or abs(ly) > 0
-
-    # Left stick → WASD (movement direction relative to facing)
-    if left_active:
-        ms.movement_angle = ms.facing_angle + math.atan2(-lx, -ly)
-        ms.has_movement = True
-        if not _joy_prev_active:
-            ms.needs_replan = True
-        _joy_prev_active = True
-    elif _joy_prev_active and not (abs(rx) > 0 or abs(ry) > 0):
-        _joy_prev_active = False
-        ms.has_movement = False
-
-    # Right stick X → Q/E (facing rotation, ~1 rad/s at full deflection)
-    if abs(rx) > 0:
-        delta = -0.02 * rx
-        ms.facing_angle += delta
-        if controller:
-            controller.delta_heading += delta
-
-    # Right stick Y → -/= (height adjustment, ~0.25/s at full deflection)
-    if abs(ry) > 0:
-        step = -0.005 * ry
-        ms.height = max(0.1, min(1.0, (ms.height if ms.height >= 0 else DEFAULT_HEIGHT) + step))

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

@@ -1,152 +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 full-body controller for Unitree G1."""
-
-import logging
-
-import numpy as np
-import onnxruntime as ort
-from huggingface_hub import hf_hub_download
-
-from lerobot.robots.unitree_g1.controllers.sonic_pipeline import (
-    CONTROL_DT,
-    DEBUG_PRINT_EVERY,
-    DEFAULT_ANGLES,
-    ENCODER_UPDATE_EVERY,
-    LM,
-    MOTION_SETS,
-    MovementState,
-    PlannerController,
-    SonicPlanner,
-    clamp_mode_params,
-    compute_kp_kd,
-    lowstate_to_obs,
-    process_joystick,
-    should_replan_request,
-    _ort_providers,
-    _snapshot_ms,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class SonicRuntime:
-    """Shared SONIC control loop state (standalone demo + locomotion controller)."""
-
-    def __init__(self, force_cpu: bool = False):
-        planner_path = hf_hub_download(repo_id="nvidia/GEAR-SONIC", filename="planner_sonic.onnx")
-        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)
-        self.use_gpu = providers[0] == "CUDAExecutionProvider"
-        so = ort.SessionOptions()
-        so.log_severity_level = 3
-
-        planner_sess = ort.InferenceSession(planner_path, sess_options=so, providers=providers)
-        encoder_sess = ort.InferenceSession(encoder_path, sess_options=so, providers=providers)
-        decoder_sess = ort.InferenceSession(decoder_path, sess_options=so, providers=providers)
-
-        self.kp, self.kd = compute_kp_kd()
-        self.ms = MovementState()
-        self.planner = SonicPlanner(planner_sess, planner_path)
-        self.controller = PlannerController(self.planner, encoder_sess, decoder_sess)
-
-        motion = self.planner.initialize(DEFAULT_ANGLES, self.ms)
-        self.controller.load_initial_motion(motion)
-        self.planner.start_subprocess(self.controller, use_gpu=self.use_gpu)
-
-        self.step = 0
-        self.replan_timer = 0.0
-        self.last_ms = _snapshot_ms(self.ms)
-
-    @property
-    def pipeline(self):
-        return self.controller
-
-    def tick(self, obs: dict, *, debug: bool | None = None, use_joystick: bool = True) -> dict:
-        if not obs:
-            self.step += 1
-            return {}
-
-        if use_joystick:
-            process_joystick(obs, self.ms, self.controller)
-        clamp_mode_params(self.ms)
-
-        if self.step > 0:
-            self.replan_timer += CONTROL_DT
-        if should_replan_request(self.ms, self.last_ms, self.replan_timer, self.step):
-            self.planner.request_replan(self.controller.ref_cursor, self.ms)
-            self.replan_timer = 0.0
-            self.ms.needs_replan = False
-            self.last_ms = _snapshot_ms(self.ms)
-
-        do_enc = self.step % ENCODER_UPDATE_EVERY == 0
-        if debug is None:
-            debug = self.step % DEBUG_PRINT_EVERY == 0
-        action = self.controller.step(obs, update_encoder=do_enc, debug=debug)
-
-        result = self.planner.try_get_new_motion()
-        if result:
-            self.controller.blend_new_motion(*result)
-
-        self.controller.advance_cursor()
-        self.step += 1
-        return action
-
-    def reset(self):
-        self.ms = MovementState()
-        self.controller.reinit_heading = True
-        self.controller.playing = True
-        self.step = 0
-        self.replan_timer = 0.0
-        self.last_ms = _snapshot_ms(self.ms)
-
-    def shutdown(self):
-        self.planner.stop_subprocess()
-
-
-class SonicWholeBodyController:
-    """Full-body SONIC controller for UnitreeG1's background controller thread."""
-
-    control_dt = CONTROL_DT
-    full_body = 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
-        self.ms = self._runtime.ms
-        logger.info(
-            "SONIC ready: %s (default mode: %s)",
-            MOTION_SETS[0][0],
-            LM(self.ms.mode).name,
-        )
-
-    def run_step(self, action: dict, lowstate) -> dict:
-        if lowstate is None:
-            return {}
-        obs = lowstate_to_obs(lowstate)
-        return self._runtime.tick(obs, debug=False)
-
-    def reset(self):
-        self._runtime.reset()
-
-    def shutdown(self):
-        self._runtime.shutdown()

src/lerobot/robots/unitree_g1/g1_utils.py

@@ -68,9 +68,8 @@ def make_locomotion_controller(name: str | None):
     if name is None:
         return None
     controllers = {
-        "GrootLocomotionController": "lerobot.robots.unitree_g1.controllers.gr00t_locomotion",
-        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.controllers.holosoma_locomotion",
-        "SonicWholeBodyController": "lerobot.robots.unitree_g1.controllers.sonic_whole_body",
+        "GrootLocomotionController": "lerobot.robots.unitree_g1.gr00t_locomotion",
+        "HolosomaLocomotionController": "lerobot.robots.unitree_g1.holosoma_locomotion",
     }
     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 lerobot.robots.unitree_g1.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 lerobot.robots.unitree_g1.g1_utils import (
+from .g1_utils import (
     REMOTE_AXES,
     G1_29_JointArmIndex,
     G1_29_JointIndex,

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -338,9 +338,6 @@ 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
@@ -377,9 +374,6 @@ class UnitreeG1(Robot):
         # Signal thread to stop and unblock any waits
         self._shutdown_event.set()
 
-        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:
             self.subscribe_thread.join(timeout=2.0)
@@ -471,11 +465,9 @@ class UnitreeG1(Robot):
     def send_action(self, action: RobotAction) -> RobotAction:
         action_to_publish = action
         if self.controller is not None:
-            self._update_controller_action(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

src/lerobot/scripts/lerobot_eval.py

@@ -72,8 +72,9 @@ from termcolor import colored
 from torch import Tensor, nn
 from tqdm import trange
 
-from lerobot.configs import parser
+from lerobot.configs import FeatureType, parser
 from lerobot.configs.eval import EvalPipelineConfig
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.envs import (
     check_env_attributes_and_types,
     close_envs,
@@ -84,7 +85,7 @@ from lerobot.envs import (
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
 from lerobot.processor import PolicyProcessorPipeline
 from lerobot.types import PolicyAction
-from lerobot.utils.constants import ACTION, DONE, OBS_STR, REWARD
+from lerobot.utils.constants import ACTION, DONE, OBS_IMAGE, OBS_IMAGES, OBS_STR, REWARD
 from lerobot.utils.device_utils import get_safe_torch_device
 from lerobot.utils.import_utils import register_third_party_plugins
 from lerobot.utils.io_utils import write_video
@@ -95,6 +96,65 @@ from lerobot.utils.utils import (
 )
 
 
+def _env_features_to_dataset_features(env_features: dict) -> dict:
+    """Convert EnvConfig.features to the dict format expected by LeRobotDataset.create()."""
+    features = {}
+    for key, ft in env_features.items():
+        shape = tuple(ft.shape)
+        if ft.type is FeatureType.VISUAL:
+            features[key] = {"dtype": "video", "shape": shape, "names": ["height", "width", "channel"]}
+        else:
+            features[key] = {"dtype": "float32", "shape": shape, "names": None}
+    features["next.reward"] = {"dtype": "float32", "shape": (1,), "names": None}
+    features["next.success"] = {"dtype": "bool", "shape": (1,), "names": None}
+    features["next.done"] = {"dtype": "bool", "shape": (1,), "names": None}
+    return features
+
+
+def _build_raw_frame(
+    raw_obs: dict,
+    env_idx: int,
+    action: np.ndarray,
+    reward: float,
+    success: bool,
+    done: bool,
+    task: str,
+    env_features: dict,
+) -> dict:
+    """Build a dataset frame from raw env observations for one env index.
+
+    Keys in the frame match the keys in env_features so they align with the
+    dataset schema created by _env_features_to_dataset_features().
+    """
+    frame: dict[str, Any] = {}
+    for key in env_features:
+        if key == ACTION:
+            continue
+        if key.startswith("next."):
+            continue
+        if "pixels" in raw_obs and isinstance(raw_obs["pixels"], dict):
+            for cam_name, img in raw_obs["pixels"].items():
+                candidate = f"{OBS_IMAGES}.{cam_name}"
+                if candidate == key:
+                    frame[key] = img[env_idx]
+            if key in frame:
+                continue
+        if "pixels" in raw_obs and not isinstance(raw_obs["pixels"], dict) and key in ("pixels", OBS_IMAGE):
+            frame[key] = raw_obs["pixels"][env_idx]
+            continue
+        if key in raw_obs and isinstance(raw_obs[key], np.ndarray):
+            val = raw_obs[key][env_idx]
+            if val.dtype == np.float64:
+                val = val.astype(np.float32)
+            frame[key] = val
+    frame[ACTION] = action
+    frame["next.reward"] = np.atleast_1d(np.float32(reward))
+    frame["next.success"] = np.atleast_1d(np.bool_(success))
+    frame["next.done"] = np.atleast_1d(np.bool_(done))
+    frame["task"] = task
+    return frame
+
+
 def rollout(
     env: gym.vector.VectorEnv,
     policy: PreTrainedPolicy,
@@ -105,6 +165,10 @@ def rollout(
     seeds: list[int] | None = None,
     return_observations: bool = False,
     render_callback: Callable[[gym.vector.VectorEnv], None] | None = None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ) -> dict:
     """Run a batched policy rollout once through a batch of environments.
 
@@ -145,6 +209,33 @@ def rollout(
     if render_callback is not None:
         render_callback(env)
 
+    recording_datasets: list[LeRobotDataset] | None = None
+    raw_observation = None
+    task_desc = ""
+    if recording_dir is not None and env_features is not None:
+        features = _env_features_to_dataset_features(env_features)
+        fps = env.unwrapped.metadata.get("render_fps", 30)
+        recording_datasets = []
+        for i in range(env.num_envs):
+            multi_env = env.num_envs > 1
+            root = str(recording_dir / f"env_{i}") if multi_env else str(recording_dir)
+            base_repo_id = recording_repo_id or "eval_recording"
+            repo_id = f"{base_repo_id}_env_{i}" if multi_env else base_repo_id
+            recording_datasets.append(
+                LeRobotDataset.create(
+                    repo_id=repo_id,
+                    fps=fps,
+                    features=features,
+                    root=root,
+                    use_videos=True,
+                )
+            )
+        raw_observation = deepcopy(observation)
+        try:
+            task_desc = list(env.call("task_description"))[0]
+        except (AttributeError, NotImplementedError):
+            task_desc = ""
+
     all_observations = []
     all_actions = []
     all_rewards = []
@@ -217,6 +308,26 @@ def rollout(
         else:
             successes = [False] * env.num_envs
 
+        if recording_datasets is not None and raw_observation is not None:
+            prev_done = done.copy()
+            for env_idx in range(env.num_envs):
+                if prev_done[env_idx]:
+                    continue
+                frame = _build_raw_frame(
+                    raw_observation,
+                    env_idx,
+                    action_numpy[env_idx],
+                    reward[env_idx],
+                    successes[env_idx],
+                    bool(terminated[env_idx] | truncated[env_idx]),
+                    task_desc,
+                    recording_datasets[env_idx].features,
+                )
+                recording_datasets[env_idx].add_frame(frame)
+                if terminated[env_idx] or truncated[env_idx]:
+                    recording_datasets[env_idx].save_episode()
+            raw_observation = deepcopy(observation)
+
         # Keep track of which environments are done so far.
         # Mark the episode as done if we reach the maximum step limit.
         # This ensures that the rollout always terminates cleanly at `max_steps`,
@@ -255,6 +366,12 @@ def rollout(
             stacked_observations[key] = torch.stack([obs[key] for obs in all_observations], dim=1)
         ret[OBS_STR] = stacked_observations
 
+    if recording_datasets is not None:
+        for ds in recording_datasets:
+            ds.finalize()
+            if recording_repo_id is not None:
+                ds.push_to_hub(private=recording_private)
+
     if hasattr(policy, "use_original_modules"):
         policy.use_original_modules()
 
@@ -273,6 +390,10 @@ def eval_policy(
     videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ) -> dict:
     """
     Args:
@@ -361,6 +482,10 @@ def eval_policy(
             seeds=list(seeds) if seeds else None,
             return_observations=return_episode_data,
             render_callback=render_frame if max_episodes_rendered > 0 else None,
+            recording_dir=recording_dir,
+            env_features=env_features,
+            recording_repo_id=recording_repo_id,
+            recording_private=recording_private,
         )
 
         # Figure out where in each rollout sequence the first done condition was encountered (results after
@@ -563,6 +688,10 @@ def eval_main(cfg: EvalPipelineConfig):
     # Create environment-specific preprocessor and postprocessor (e.g., for LIBERO environments)
     env_preprocessor, env_postprocessor = make_env_pre_post_processors(env_cfg=cfg.env, policy_cfg=cfg.policy)
 
+    recording_dir = Path(cfg.output_dir) / "recordings" if cfg.eval.recording else None
+    max_episodes_rendered = 0 if cfg.eval.recording else 10
+    videos_dir = None if cfg.eval.recording else Path(cfg.output_dir) / "videos"
+
     with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
         info = eval_policy_all(
             envs=envs,
@@ -572,10 +701,15 @@ def eval_main(cfg: EvalPipelineConfig):
             preprocessor=preprocessor,
             postprocessor=postprocessor,
             n_episodes=cfg.eval.n_episodes,
-            max_episodes_rendered=10,
-            videos_dir=Path(cfg.output_dir) / "videos",
+            max_episodes_rendered=max_episodes_rendered,
+            videos_dir=videos_dir,
+            return_episode_data=False,
             start_seed=cfg.seed,
             max_parallel_tasks=cfg.env.max_parallel_tasks,
+            recording_dir=recording_dir,
+            env_features=cfg.env.features if cfg.eval.recording else None,
+            recording_repo_id=cfg.eval.recording_repo_id,
+            recording_private=cfg.eval.recording_private,
         )
         print("Overall Aggregated Metrics:")
         print(info["overall"])
@@ -618,6 +752,10 @@ def eval_one(
     videos_dir: Path | None,
     return_episode_data: bool,
     start_seed: int | None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ) -> TaskMetrics:
     """Evaluates one task_id of one suite using the provided vec env."""
 
@@ -635,6 +773,10 @@ def eval_one(
         videos_dir=task_videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
+        recording_dir=recording_dir,
+        env_features=env_features,
+        recording_repo_id=recording_repo_id,
+        recording_private=recording_private,
     )
 
     per_episode = task_result["per_episode"]
@@ -661,6 +803,10 @@ def run_one(
     videos_dir: Path | None,
     return_episode_data: bool,
     start_seed: int | None,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
 ):
     """
     Run eval_one for a single (task_group, task_id, env).
@@ -672,7 +818,13 @@ def run_one(
         task_videos_dir = videos_dir / f"{task_group}_{task_id}"
         task_videos_dir.mkdir(parents=True, exist_ok=True)
 
-    # Call the existing eval_one (assumed to return TaskMetrics-like dict)
+    task_recording_dir = None
+    task_repo_id = None
+    if recording_dir is not None and env_features is not None:
+        task_recording_dir = recording_dir / f"{task_group}_{task_id}"
+        if recording_repo_id is not None:
+            task_repo_id = f"{recording_repo_id}_{task_group}_{task_id}"
+
     metrics = eval_one(
         env,
         policy=policy,
@@ -685,8 +837,12 @@ def run_one(
         videos_dir=task_videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
+        recording_dir=task_recording_dir,
+        env_features=env_features,
+        recording_repo_id=task_repo_id,
+        recording_private=recording_private,
     )
-    # ensure we always provide video_paths key to simplify accumulation
+
     if max_episodes_rendered > 0:
         metrics.setdefault("video_paths", [])
     return task_group, task_id, metrics
@@ -702,6 +858,10 @@ def eval_policy_all(
     n_episodes: int,
     *,
     max_episodes_rendered: int = 0,
+    recording_dir: Path | None = None,
+    env_features: dict | None = None,
+    recording_repo_id: str | None = None,
+    recording_private: bool = False,
     videos_dir: Path | None = None,
     return_episode_data: bool = False,
     start_seed: int | None = None,
@@ -761,6 +921,10 @@ def eval_policy_all(
         videos_dir=videos_dir,
         return_episode_data=return_episode_data,
         start_seed=start_seed,
+        recording_dir=recording_dir,
+        env_features=env_features,
+        recording_repo_id=recording_repo_id,
+        recording_private=recording_private,
     )
 
     if max_parallel_tasks <= 1:

tests/datasets/test_datasets.py

@@ -51,7 +51,7 @@ from lerobot.robots import make_robot_from_config
 from lerobot.transforms import ImageTransforms, ImageTransformsConfig
 from lerobot.utils.constants import ACTION, DONE, OBS_IMAGES, OBS_STATE, OBS_STR, REWARD
 from lerobot.utils.feature_utils import hw_to_dataset_features
-from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_REPO_ID
+from tests.fixtures.constants import DUMMY_CHW, DUMMY_HWC, DUMMY_MOTOR_FEATURES, DUMMY_REPO_ID
 from tests.mocks.mock_robot import MockRobotConfig
 from tests.utils import require_x86_64_kernel
 
@@ -133,6 +133,21 @@ def test_dataset_feature_with_forward_slash_raises_error():
         )
 
 
+def test_create_does_not_mutate_input_features(tmp_path, empty_lerobot_dataset_factory):
+    # ``create`` must deep-copy features so a dataset built from another's features stays independent.
+    dataset = empty_lerobot_dataset_factory(
+        root=tmp_path / "ds1", features=DUMMY_MOTOR_FEATURES, use_videos=False
+    )
+    dataset_copy = empty_lerobot_dataset_factory(
+        root=tmp_path / "ds2", features=dataset.meta.features, use_videos=False
+    )
+
+    original_shape = dataset.meta.info.features["state"]["shape"]
+    dataset_copy.meta.info.features["state"]["shape"] = (999,)
+
+    assert dataset.meta.info.features["state"]["shape"] == original_shape
+
+
 def test_add_frame_missing_task(tmp_path, empty_lerobot_dataset_factory):
     features = {"state": {"dtype": "float32", "shape": (1,), "names": None}}
     dataset = empty_lerobot_dataset_factory(root=tmp_path / "test", features=features)