feat(policies): early stopping + termination

The decode loop always runs all max_decoding_steps (256) tokens, but
detokenize_actions() cuts the output at the first "|", so most of them get
generated and then thrown away. On LIBERO the marker lands around token 25-35.

Stopping there gives the same actions ~4x faster end to end. Checked on
libero_10: 50/50 episodes came out identical to the old path, same success
and same state trajectories.

.github/workflows/benchmark_tests.yml

@@ -53,7 +53,7 @@ permissions:
   contents: read
 
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
 
 # Cancel in-flight runs for the same branch/PR.

.github/workflows/docker_publish.yml

@@ -27,7 +27,7 @@ on:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME_CPU: huggingface/lerobot-cpu:latest
   DOCKER_IMAGE_NAME_GPU: huggingface/lerobot-gpu:latest

.github/workflows/fast_tests.yml

@@ -48,7 +48,7 @@ permissions:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
 
 # Ensures that only the latest commit for a PR or branch is built, canceling older runs.

.github/workflows/full_tests.yml

@@ -37,7 +37,7 @@ permissions:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu
 

.github/workflows/latest_deps_tests.yml

@@ -27,7 +27,7 @@ on:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
   DOCKER_IMAGE_NAME: huggingface/lerobot-gpu:latest-deps
 

.github/workflows/release.yml

@@ -21,7 +21,7 @@ on:
 
 # Sets up the environment variables
 env:
-  UV_VERSION: "0.8.0"
+  UV_VERSION: "0.11.30"
   PYTHON_VERSION: "3.12"
 
 jobs:

docs/source/libero.mdx

@@ -92,6 +92,20 @@ LIBERO supports two control modes — `relative` (default) and `absolute`. Diffe
 --env.control_mode=relative   # or "absolute"
 ```
 
+### Reset performance
+
+By default, LeRobot preserves LIBERO's hard-reset behavior. With fixed initial
+states enabled, you can opt into soft resets to skip rebuilding the simulator
+model and renderer on every episode:
+
+```bash
+--env.init_states=true --env.hard_reset=false
+```
+
+Soft resets are faster but are not bit-identical to hard resets after the
+environment's settling steps, so camera observations and policy results may
+differ slightly. Use hard resets when reproducing benchmark results.
+
 ### Policy inputs and outputs
 
 **Observations:**

docs/source/libero_plus.mdx

@@ -134,6 +134,20 @@ LIBERO-plus supports two control modes — `relative` (default) and `absolute`.
 --env.control_mode=relative   # or "absolute"
 ```
 
+### Reset performance
+
+By default, LeRobot preserves LIBERO's hard-reset behavior. With fixed initial
+states enabled, you can opt into soft resets to skip rebuilding the simulator
+model and renderer on every episode:
+
+```bash
+--env.init_states=true --env.hard_reset=false
+```
+
+Soft resets are faster but are not bit-identical to hard resets after the
+environment's settling steps, so camera observations and policy results may
+differ slightly. Use hard resets when reproducing benchmark results.
+
 ### Policy inputs and outputs
 
 **Observations:**

examples/openarm/README.md

@@ -1,75 +0,0 @@
-# OpenArm — Episode Replay in Simulation
-
-Replay a recorded bimanual-[OpenArm](https://openarm.dev) episode into an mp4 by driving the
-official OpenArm MuJoCo model directly from a LeRobot dataset's recorded joint states. This is
-a visual sanity check for recorded/commanded trajectories and for the end-effector kinematics
-exposed by `OpenArmFollower.make_kinematics()` (see the [OpenArm docs](../../docs/source/openarm.mdx)).
-
-## Model provenance
-
-Everything is pulled from Enactic's official, Apache-2.0 OpenArm repositories — nothing is
-vendored into LeRobot:
-
-| Asset                                | Source                                                                          | License    |
-| ------------------------------------ | ------------------------------------------------------------------------------- | ---------- |
-| MuJoCo MJCF (used here)              | [`enactic/openarm_mujoco`](https://github.com/enactic/openarm_mujoco)           | Apache-2.0 |
-| URDF / xacro (for `RobotKinematics`) | [`enactic/openarm_description`](https://github.com/enactic/openarm_description) | Apache-2.0 |
-
-Use the **v1** MuJoCo revision (`v1/openarm_bimanual.xml`). v2 is a different wrist hardware
-revision (DM3507) and will look sign-flipped when replaying v1 recordings.
-
-## Setup
-
-```bash
-# LeRobot in your env (see https://huggingface.co/docs/lerobot/installation)
-# Plus the sim/replay deps:
-pip install mujoco av pandas
-
-# Get the OpenArm MuJoCo model (either works):
-pip install openarm-mujoco                                   # installs models under <prefix>/share/openarm_mujoco/
-# or
-git clone https://github.com/enactic/openarm_mujoco.git      # then pass --mjcf .../v1/openarm_bimanual.xml
-```
-
-The script auto-locates the model in this order: `--mjcf` arg → `$OPENARM_MJCF` →
-`<sys.prefix>/share/openarm_mujoco/v1/openarm_bimanual.xml`.
-
-## Dataset layout
-
-`observation.state` must be the 16-D bimanual vector (degrees):
-
-```
-right_joint_1..7, right_gripper, left_joint_1..7, left_gripper
-```
-
-Only the 14 arm joints affect the rendered pose; the two gripper scalars drive the fingers.
-
-## Run
-
-Headless rendering needs `MUJOCO_GL=egl`, and MuJoCo's GL libs on `LD_LIBRARY_PATH`
-(in conda: `$CONDA_PREFIX/lib`).
-
-```bash
-# Replay episode 1 of a local LeRobot v3.0 dataset
-LD_LIBRARY_PATH=$CONDA_PREFIX/lib MUJOCO_GL=egl \
-python -m examples.openarm.render_episode \
-    --dataset data/folding_src_meta \
-    --episode 1 \
-    --out openarm_ep1.mp4
-
-# No dataset handy? Smoke-test with a synthetic wave:
-LD_LIBRARY_PATH=$CONDA_PREFIX/lib MUJOCO_GL=egl \
-python -m examples.openarm.render_episode --demo --out openarm_demo.mp4
-```
-
-Useful flags: `--fps` (default 30), `--width` / `--height` (default 960×720), `--mjcf` to point
-at an explicit model file.
-
-## Troubleshooting
-
-| Symptom                               | Fix                                                                    |
-| ------------------------------------- | ---------------------------------------------------------------------- |
-| `Could not find the OpenArm v1 MJCF`  | Pass `--mjcf`, set `$OPENARM_MJCF`, or install/clone `openarm_mujoco`. |
-| `libEGL`/`GLEW` / blank window errors | Ensure `MUJOCO_GL=egl` and `LD_LIBRARY_PATH=$CONDA_PREFIX/lib`.        |
-| Wrists look mirrored / flipped        | You are on the v2 model; switch to **v1**.                             |
-| `KeyError: 'observation.state'`       | Dataset isn't in the expected 16-D bimanual layout.                    |

examples/openarm/render_episode.py

@@ -1,217 +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.
-
-"""Replay a recorded bimanual-OpenArm episode into an mp4, in simulation.
-
-This drives the official OpenArm MuJoCo model (``enactic/openarm_mujoco``, v1) directly
-from a LeRobot dataset's recorded ``observation.state`` and renders a headless video. It is
-a visual sanity check that a recorded/commanded joint trajectory is what you think it is --
-the same forward-kinematics path used to expose end-effector poses (see the OpenArm
-end-effector kinematics helper ``OpenArmFollower.make_kinematics``).
-
-The ``observation.state`` is expected in the 16-D bimanual layout (degrees):
-    right_joint_1..7, right_gripper, left_joint_1..7, left_gripper
-
-Model (Apache-2.0): https://github.com/enactic/openarm_mujoco  (use the **v1** revision;
-v2 is a different wrist hardware revision and will look sign-flipped on v1 recordings).
-
-Examples:
-    # replay episode 1 of a local LeRobot v3.0 dataset
-    LD_LIBRARY_PATH=$CONDA_PREFIX/lib MUJOCO_GL=egl python -m examples.openarm.render_episode \
-        --dataset data/folding_src_meta --episode 1 --out openarm_ep1.mp4
-
-    # smoke test with a synthetic wave (no dataset / model joints only)
-    LD_LIBRARY_PATH=$CONDA_PREFIX/lib MUJOCO_GL=egl python -m examples.openarm.render_episode \
-        --demo --out openarm_demo.mp4
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-import sys
-from pathlib import Path
-
-os.environ.setdefault("MUJOCO_GL", "egl")  # headless GPU rendering
-
-import numpy as np
-
-# Policy/dataset state layout: 16-D, degrees.
-POLICY_ORDER = [
-    *(f"right_joint_{i}" for i in range(1, 8)),
-    "right_gripper",
-    *(f"left_joint_{i}" for i in range(1, 8)),
-    "left_gripper",
-]
-# MuJoCo joint name for each of the 14 arm entries (grippers handled separately).
-ARM_MAP = {
-    **{f"right_joint_{i}": f"openarm_right_joint{i}" for i in range(1, 8)},
-    **{f"left_joint_{i}": f"openarm_left_joint{i}" for i in range(1, 8)},
-}
-RIGHT_GRIPPER_IDX, LEFT_GRIPPER_IDX = 7, 15
-GRIP_FULL_DEG = 65.0  # follower gripper limit magnitude -> fully open
-
-
-def locate_mjcf(explicit: str | None) -> str:
-    """Resolve the OpenArm v1 bimanual MJCF path.
-
-    Priority: --mjcf arg, then $OPENARM_MJCF, then the file installed by the
-    ``openarm_mujoco`` pip package under ``<prefix>/share/openarm_mujoco/v1``.
-    """
-    if explicit:
-        return explicit
-    if os.environ.get("OPENARM_MJCF"):
-        return os.environ["OPENARM_MJCF"]
-    for prefix in (sys.prefix, os.environ.get("CONDA_PREFIX", "")):
-        if not prefix:
-            continue
-        cand = Path(prefix) / "share" / "openarm_mujoco" / "v1" / "openarm_bimanual.xml"
-        if cand.exists():
-            return str(cand)
-    raise SystemExit(
-        "Could not find the OpenArm v1 MJCF. Pass --mjcf /path/to/v1/openarm_bimanual.xml, "
-        "set $OPENARM_MJCF, or clone https://github.com/enactic/openarm_mujoco."
-    )
-
-
-def load_state_from_dataset(root: str, ep: int) -> np.ndarray:
-    """Read one episode's recorded observation.state (N, 16; degrees) from a LeRobot v3.0 root."""
-    import pandas as pd
-
-    root = Path(root)
-    ep_meta = pd.read_parquet(root / "meta" / "episodes" / "chunk-000" / "file-000.parquet")
-    row = ep_meta[ep_meta["episode_index"] == ep].iloc[0]
-    a, b = int(row["dataset_from_index"]), int(row["dataset_to_index"])
-    dchunk, dfile = int(row["data/chunk_index"]), int(row["data/file_index"])
-    df = pd.read_parquet(root / "data" / f"chunk-{dchunk:03d}" / f"file-{dfile:03d}.parquet")
-    df = df[(df["index"] >= a) & (df["index"] < b)].sort_values("frame_index")
-    return np.stack(df["observation.state"].to_numpy()).astype(np.float32)
-
-
-def encode_mp4(frames: list[np.ndarray], path: str, fps: int) -> None:
-    import av
-
-    h, w = frames[0].shape[:2]
-    container = av.open(path, mode="w")
-    stream = container.add_stream("libx264", rate=fps)
-    stream.width, stream.height, stream.pix_fmt = w, h, "yuv420p"
-    for f in frames:
-        frame = av.VideoFrame.from_ndarray(np.ascontiguousarray(f), format="rgb24")
-        for pkt in stream.encode(frame):
-            container.mux(pkt)
-    for pkt in stream.encode():
-        container.mux(pkt)
-    container.close()
-
-
-def main() -> None:
-    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawTextHelpFormatter)
-    ap.add_argument("--dataset", default=None, help="LeRobot v3.0 dataset root (meta/ + data/)")
-    ap.add_argument("--episode", type=int, default=0)
-    ap.add_argument("--demo", action="store_true", help="drive a synthetic wave (no dataset)")
-    ap.add_argument("--mjcf", default=None, help="path to v1/openarm_bimanual.xml (see locate_mjcf)")
-    ap.add_argument("--out", default="openarm_episode.mp4")
-    ap.add_argument("--fps", type=int, default=30)
-    ap.add_argument("--width", type=int, default=960)
-    ap.add_argument("--height", type=int, default=720)
-    args = ap.parse_args()
-
-    import mujoco
-
-    model = mujoco.MjModel.from_xml_path(locate_mjcf(args.mjcf))
-    model.vis.global_.offwidth = max(model.vis.global_.offwidth, args.width)
-    model.vis.global_.offheight = max(model.vis.global_.offheight, args.height)
-    data = mujoco.MjData(model)
-
-    qadr = {
-        pk: int(model.jnt_qposadr[mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, mj)])
-        for pk, mj in ARM_MAP.items()
-    }
-
-    def finger_adr(names):
-        out = []
-        for nm in names:
-            jid = mujoco.mj_name2id(model, mujoco.mjtObj.mjOBJ_JOINT, nm)
-            out.append((int(model.jnt_qposadr[jid]), model.jnt_range[jid].copy(), int(model.jnt_type[jid])))
-        return out
-
-    right_fingers = finger_adr(["openarm_right_finger_joint1", "openarm_right_finger_joint2"])
-    left_fingers = finger_adr(["openarm_left_finger_joint1", "openarm_left_finger_joint2"])
-    hinge_type = int(mujoco.mjtJoint.mjJNT_HINGE)
-
-    def finger_target(gripper_deg, rng, jtype):
-        opening = min(1.0, abs(gripper_deg) / GRIP_FULL_DEG)  # 0=closed .. 1=open
-        if jtype == hinge_type:  # hinge in radians; sign encodes side via range direction
-            lo, hi = rng
-            mag = np.deg2rad(min(abs(gripper_deg), GRIP_FULL_DEG))
-            return np.clip(-mag if lo < 0 else mag, lo, hi)
-        return rng[0] + opening * (rng[1] - rng[0])  # slide (v1): lo=closed .. hi=open
-
-    if args.demo:
-        n_frames = 120
-        traj = np.zeros((n_frames, 16), np.float32)
-        wave = 40.0 * np.sin(np.linspace(0, 2 * np.pi, n_frames))
-        for i, pk in enumerate(POLICY_ORDER):
-            if pk in qadr:
-                traj[:, i] = wave * (0.5 + 0.5 * (i % 3))
-    elif args.dataset:
-        traj = load_state_from_dataset(args.dataset, args.episode)
-    else:
-        raise SystemExit("provide --dataset <root> (with --episode) or --demo")
-    n_frames = traj.shape[0]
-    print(f"driving {n_frames} frames")
-
-    # Auto-frame the arms (exclude pedestal/world) from body positions at the mid pose.
-    mid = n_frames // 2
-    for i, pk in enumerate(POLICY_ORDER):
-        if pk in qadr:
-            data.qpos[qadr[pk]] = np.deg2rad(traj[mid, i])
-    mujoco.mj_forward(model, data)
-    arm_pts = [
-        data.xpos[b].copy()
-        for b in range(model.nbody)
-        if (mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_BODY, b) or "").startswith("openarm")
-        and "base" not in (mujoco.mj_id2name(model, mujoco.mjtObj.mjOBJ_BODY, b) or "")
-    ]
-    arm_pts = np.array(arm_pts) if arm_pts else data.xpos[1:]
-    lo, hi = arm_pts.min(0), arm_pts.max(0)
-    cam = mujoco.MjvCamera()
-    mujoco.mjv_defaultCamera(cam)
-    cam.azimuth, cam.elevation = 150.0, -20.0
-    cam.distance = max(0.8, float(np.linalg.norm(hi - lo)) * 1.3)
-    cam.lookat[:] = (lo + hi) / 2.0
-
-    renderer = mujoco.Renderer(model, height=args.height, width=args.width)
-    frames = []
-    for t in range(n_frames):
-        for i, pk in enumerate(POLICY_ORDER):
-            if pk in qadr:
-                data.qpos[qadr[pk]] = np.deg2rad(traj[t, i])
-        for adr, rng, jt in right_fingers:
-            data.qpos[adr] = finger_target(float(traj[t, RIGHT_GRIPPER_IDX]), rng, jt)
-        for adr, rng, jt in left_fingers:
-            data.qpos[adr] = finger_target(float(traj[t, LEFT_GRIPPER_IDX]), rng, jt)
-        mujoco.mj_forward(model, data)
-        renderer.update_scene(data, camera=cam)
-        frames.append(renderer.render())
-    renderer.close()
-
-    encode_mp4(frames, args.out, args.fps)
-    print(f"wrote {args.out}  ({n_frames} frames @ {args.fps} fps, {args.width}x{args.height})")
-
-
-if __name__ == "__main__":
-    main()

pyproject.toml

@@ -87,7 +87,7 @@ dependencies = [
 
     # Build tools (required by opencv-python-headless on some platforms)
     "cmake>=3.29.0.1,<4.2.0",
-    "setuptools>=71.0.0,<81.0.0",
+    "setuptools>=71.0.0,<82.0.0",  # torch 2.11 requires setuptools<82; a higher cap makes the resolver downgrade torch
 ]
 
 # Optional dependencies
@@ -261,7 +261,7 @@ annotations = [
 # Development
 dev = ["pre-commit>=3.7.0,<5.0.0", "debugpy>=1.8.1,<1.9.0", "lerobot[grpcio-dep]", "grpcio-tools>=1.73.1,<2.0.0", "mypy>=1.19.1", "ruff>=0.14.1", "lerobot[notebook]"]
 notebook = ["jupyter>=1.0.0,<2.0.0", "ipykernel>=6.0.0,<7.0.0"]
-test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
+test = ["pytest>=8.1.0,<10.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0.0,<8.0.0", "mock-serial>=0.0.1,<0.1.0 ; sys_platform != 'win32'"]
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation

src/lerobot/envs/configs.py

@@ -328,6 +328,7 @@ class LiberoEnv(EnvConfig):
     render_mode: str = "rgb_array"
     camera_name: str = "agentview_image,robot0_eye_in_hand_image"
     init_states: bool = True
+    hard_reset: bool = True
     camera_name_mapping: dict[str, str] | None = None
     observation_height: int = 360
     observation_width: int = 360
@@ -356,6 +357,8 @@ class LiberoEnv(EnvConfig):
     def __post_init__(self):
         if self.fps <= 0:
             raise ValueError(f"fps must be positive, got {self.fps}")
+        if not self.hard_reset and not self.init_states:
+            raise ValueError("hard_reset=False requires init_states=True")
 
         if self.obs_type == "pixels":
             self.features[LIBERO_KEY_PIXELS_AGENTVIEW] = PolicyFeature(
@@ -416,6 +419,7 @@ class LiberoEnv(EnvConfig):
             "observation_height": self.observation_height,
             "observation_width": self.observation_width,
             "control_freq": self.fps,
+            "hard_reset": self.hard_reset,
         }
         if self.task_ids is not None:
             kwargs["task_ids"] = self.task_ids

src/lerobot/envs/libero.py

@@ -128,10 +128,13 @@ class LiberoEnv(gym.Env):
         control_freq: int = 20,
         control_mode: str = "relative",
         is_libero_plus: bool = False,
+        hard_reset: bool = True,
     ):
         super().__init__()
         if control_freq <= 0:
             raise ValueError(f"control_freq must be positive, got {control_freq}")
+        if not hard_reset and not init_states:
+            raise ValueError("hard_reset=False requires init_states=True")
         self.task_id = task_id
         self.is_libero_plus = is_libero_plus
         self.obs_type = obs_type
@@ -158,6 +161,7 @@ class LiberoEnv(gym.Env):
         self.camera_name_mapping = camera_name_mapping
         self.num_steps_wait = num_steps_wait
         self.control_freq = control_freq
+        self.hard_reset = hard_reset
         self.episode_index = episode_index
         self.episode_length = episode_length
         # Load once and keep
@@ -265,6 +269,9 @@ class LiberoEnv(gym.Env):
             camera_heights=self.observation_height,
             camera_widths=self.observation_width,
             control_freq=self.control_freq,
+            # Soft resets skip LIBERO's model and renderer rebuild. They are opt-in
+            # because settle steps can make their observations differ from hard resets.
+            hard_reset=self.hard_reset,
         )
         env.reset()
         self._env = env
@@ -377,8 +384,9 @@ class LiberoEnv(gym.Env):
             }
         )
         observation = self._format_raw_obs(raw_obs)
-        if terminated:
-            self.reset()
+        # Return the terminal observation unchanged. The caller owns resetting after
+        # termination; vector envs created below use NEXT_STEP autoreset. Resetting here
+        # would therefore reset twice and skip an initial state.
         truncated = False
         return observation, reward, terminated, truncated, info
 
@@ -476,6 +484,7 @@ def create_libero_envs(
         print(f"Restricting to task_ids={task_ids_filter}")
 
     is_async = env_cls is gym.vector.AsyncVectorEnv
+    is_sync = env_cls is gym.vector.SyncVectorEnv
 
     out: dict[str, dict[int, Any]] = defaultdict(dict)
     for suite_name in suite_names:
@@ -512,6 +521,10 @@ def create_libero_envs(
                     cached_act_space = lazy.action_space
                     cached_metadata = lazy.metadata
                 out[suite_name][tid] = lazy
+            elif is_sync:
+                out[suite_name][tid] = gym.vector.SyncVectorEnv(
+                    fns, autoreset_mode=gym.vector.AutoresetMode.NEXT_STEP
+                )
             else:
                 out[suite_name][tid] = env_cls(fns)
             print(f"Built vec env | suite={suite_name} | task_id={tid} | n_envs={n_envs}")

src/lerobot/envs/utils.py

@@ -212,7 +212,12 @@ class _LazyAsyncVectorEnv:
 
     def _ensure(self) -> None:
         if self._env is None:
-            self._env = gym.vector.AsyncVectorEnv(self._env_fns, context="forkserver", shared_memory=True)
+            self._env = gym.vector.AsyncVectorEnv(
+                self._env_fns,
+                context="forkserver",
+                shared_memory=True,
+                autoreset_mode=gym.vector.AutoresetMode.NEXT_STEP,
+            )
 
     @property
     def unwrapped(self):

src/lerobot/policies/pi0_fast/modeling_pi0_fast.py

@@ -604,6 +604,12 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
     ) -> torch.Tensor:
         """
         Optimized autoregressive decoding for FAST tokens using KV Caching.
+
+        Greedy decoding stops once every sequence emits the end-of-action marker. The
+        returned tensor keeps its fixed shape, with positions not generated after the
+        batch-wide stop left zero-filled. Stochastic decoding always runs to
+        ``max_decoding_steps`` so early stopping does not change the RNG state used by
+        subsequent calls.
         """
         if max_decoding_steps is None:
             max_decoding_steps = self.config.max_action_tokens
@@ -612,6 +618,12 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         device = tokens.device
         lm_head = self.paligemma_with_expert.paligemma.lm_head
 
+        # detokenize_actions() cuts at the first "|", so greedy decoding can stop once
+        # every sequence has emitted it. Keep stochastic decoding unchanged because
+        # skipping multinomial calls would shift the RNG state for subsequent calls.
+        end_of_action_token_id = self._paligemma_tokenizer.convert_tokens_to_ids("|")
+        finished = torch.zeros(bsize, dtype=torch.bool, device=device) if temperature == 0 else None
+
         # --- 1. PREFILL PHASE ---
         # Process Images + Text Prompt + BOS token once to populate the KV cache.
 
@@ -663,6 +675,10 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
         # Initialize storage for generated tokens
         generated_action_tokens = torch.zeros((bsize, max_decoding_steps), dtype=torch.long, device=device)
         generated_action_tokens[:, 0] = next_token.squeeze(-1)
+        if finished is not None:
+            finished |= next_token.squeeze(-1) == end_of_action_token_id
+            if bool(finished.all()):
+                return generated_action_tokens
 
         # Track valid tokens mask (0 for pad, 1 for valid)
         # We need this to tell the new token what it can attend to (images + text + past actions)
@@ -713,6 +729,11 @@ class PI0FastPytorch(nn.Module):  # see openpi `PI0Pytorch`
 
             generated_action_tokens[:, t] = next_token.squeeze(-1)
 
+            if finished is not None:
+                finished |= next_token.squeeze(-1) == end_of_action_token_id
+                if bool(finished.all()):
+                    break
+
         return generated_action_tokens
 
 

src/lerobot/robots/openarm_follower/config_openarm_follower.py

@@ -74,14 +74,6 @@ class OpenArmFollowerConfigBase:
     # Set to a positive scalar for all motors, or a dict mapping motor names to limits
     max_relative_target: float | dict[str, float] | None = None
 
-    # End-effector kinematics (optional). Point `urdf_path` at the OpenArm URDF and set
-    # `target_frame_name` to the end-effector link in that URDF to enable forward/inverse
-    # kinematics in Cartesian (EE) space -- via `lerobot.model.RobotKinematics` and the
-    # shared FK/IK processor steps. Left as None, the robot behaves exactly as before
-    # (joint space only). The URDF is user-supplied, so no large asset is vendored here.
-    urdf_path: str | None = None
-    target_frame_name: str | None = None
-
     # Camera configurations
     cameras: dict[str, CameraConfig] = field(default_factory=dict)
 

src/lerobot/robots/openarm_follower/openarm_follower.py

@@ -17,7 +17,7 @@
 import logging
 import time
 from functools import cached_property
-from typing import TYPE_CHECKING, Any
+from typing import Any
 
 from lerobot.cameras import make_cameras_from_configs
 from lerobot.lerobot_types import RobotAction, RobotObservation
@@ -25,9 +25,6 @@ from lerobot.motors import Motor, MotorCalibration, MotorNormMode
 from lerobot.motors.damiao import DamiaoMotorsBus
 from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
 
-if TYPE_CHECKING:
-    from lerobot.model import RobotKinematics
-
 from ..robot import Robot
 from ..utils import ensure_safe_goal_position
 from .config_openarm_follower import (
@@ -123,33 +120,6 @@ class OpenArmFollower(Robot):
         """Action features."""
         return self._motors_ft
 
-    @property
-    def arm_motor_names(self) -> list[str]:
-        """Arm joints forming the kinematic chain to the end-effector (excludes the gripper)."""
-        return [motor for motor in self.bus.motors if motor != "gripper"]
-
-    def make_kinematics(self) -> "RobotKinematics":
-        """Build a solver for end-effector forward/inverse kinematics.
-
-        Requires ``config.urdf_path`` (path to the OpenArm URDF) and
-        ``config.target_frame_name`` (the end-effector link in that URDF). Pair the returned
-        solver with the shared FK/IK processor steps in
-        ``lerobot.robots.so_follower.robot_kinematic_processor`` to record or command the arm
-        in end-effector (Cartesian) space instead of raw joint angles.
-        """
-        from lerobot.model import RobotKinematics
-
-        if self.config.urdf_path is None or self.config.target_frame_name is None:
-            raise ValueError(
-                "OpenArm end-effector kinematics require config.urdf_path and "
-                "config.target_frame_name (the OpenArm URDF and its end-effector link)."
-            )
-        return RobotKinematics(
-            urdf_path=self.config.urdf_path,
-            target_frame_name=self.config.target_frame_name,
-            joint_names=self.arm_motor_names,
-        )
-
     @property
     def is_connected(self) -> bool:
         """Check if robot is connected."""

src/lerobot/robots/so_follower/robot_kinematic_processor.py

@@ -77,11 +77,13 @@ class EEReferenceAndDelta(RobotActionProcessorStep):
     _command_when_disabled: np.ndarray | None = field(default=None, init=False, repr=False)
 
     def action(self, action: RobotAction) -> RobotAction:
-        observation = self.transition.get(TransitionKey.OBSERVATION).copy()
+        raw_observation = self.transition.get(TransitionKey.OBSERVATION)
 
-        if observation is None:
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         if self.use_ik_solution and "IK_solution" in self.transition.get(TransitionKey.COMPLEMENTARY_DATA):
             q_raw = self.transition.get(TransitionKey.COMPLEMENTARY_DATA)["IK_solution"]
         else:
@@ -311,10 +313,12 @@ class InverseKinematicsEEToJoints(RobotActionProcessorStep):
                 "Missing required end-effector pose components: ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_pos must all be present in action"
             )
 
-        observation = self.transition.get(TransitionKey.OBSERVATION).copy()
-        if observation is None:
+        raw_observation = self.transition.get(TransitionKey.OBSERVATION)
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         q_raw = np.array(
             [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")],
             dtype=float,
@@ -391,13 +395,15 @@ class GripperVelocityToJoint(RobotActionProcessorStep):
     discrete_gripper: bool = False
 
     def action(self, action: RobotAction) -> RobotAction:
-        observation = self.transition.get(TransitionKey.OBSERVATION).copy()
+        raw_observation = self.transition.get(TransitionKey.OBSERVATION)
 
         gripper_vel = action.pop("ee.gripper_vel")
 
-        if observation is None:
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         q_raw = np.array(
             [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")],
             dtype=float,
@@ -583,10 +589,12 @@ class InverseKinematicsRLStep(ProcessorStep):
                 "Missing required end-effector pose components: ee.x, ee.y, ee.z, ee.wx, ee.wy, ee.wz, ee.gripper_pos must all be present in action"
             )
 
-        observation = new_transition.get(TransitionKey.OBSERVATION).copy()
-        if observation is None:
+        raw_observation = new_transition.get(TransitionKey.OBSERVATION)
+        if raw_observation is None:
             raise ValueError("Joints observation is require for computing robot kinematics")
 
+        observation = raw_observation.copy()
+
         q_raw = np.array(
             [float(v) for k, v in observation.items() if isinstance(k, str) and k.endswith(".pos")],
             dtype=float,

src/lerobot/scripts/lerobot_edit_dataset.py

@@ -240,6 +240,7 @@ Using JSON config file:
 
 import abc
 import logging
+import os
 import shutil
 import sys
 from dataclasses import dataclass, field
@@ -384,24 +385,35 @@ def _resolve_io_paths(
     return output_repo_id, input_path, output_path
 
 
+def _is_in_place(input_path: Path, output_path: Path) -> bool:
+    """Whether both paths point to the same dataset directory.
+
+    Uses os.path.samefile (device+inode) which is robust to case-insensitive filesystems, hardlinks
+    and symlinks.
+    """
+    try:
+        return os.path.samefile(input_path, output_path)
+    except OSError:
+        return False
+
+
 def get_output_path(
     repo_id: str,
     new_repo_id: str | None,
     root: Path | str | None,
     new_root: Path | str | None,
-) -> tuple[str, Path]:
+) -> tuple[str, Path, Path | None]:
     output_repo_id, input_path, output_path = _resolve_io_paths(repo_id, new_repo_id, root, new_root)
 
-    # In case of in-place modification, create a backup of the original dataset (if it exists)
-    if output_path == input_path:
+    # In case of in-place modification, create a backup of the original dataset (if it exists).
+    backup_path: Path | None = None
+    if _is_in_place(input_path, output_path):
         backup_path = input_path.with_name(input_path.name + "_old")
+        if backup_path.exists():
+            shutil.rmtree(backup_path)
+        shutil.move(input_path, backup_path)
 
-        if input_path.exists():
-            if backup_path.exists():
-                shutil.rmtree(backup_path)
-            shutil.move(input_path, backup_path)
-
-    return output_repo_id, output_path
+    return output_repo_id, output_path, backup_path
 
 
 def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
@@ -412,7 +424,7 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
         raise ValueError("episode_indices must be specified for delete_episodes operation")
 
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
-    output_repo_id, output_dir = get_output_path(
+    output_repo_id, output_dir, backup_path = get_output_path(
         cfg.repo_id,
         new_repo_id=cfg.new_repo_id,
         root=cfg.root,
@@ -420,8 +432,8 @@ def handle_delete_episodes(cfg: EditDatasetConfig) -> None:
     )
 
     # In case of in-place modification, make the dataset point to the backup directory
-    if output_dir == dataset.root:
-        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
+    if backup_path is not None:
+        dataset.root = backup_path
 
     logging.info(f"Deleting episodes {cfg.operation.episode_indices} from {cfg.repo_id}")
     new_dataset = delete_episodes(
@@ -525,7 +537,7 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
         raise ValueError("feature_names must be specified for remove_feature operation")
 
     dataset = LeRobotDataset(cfg.repo_id, root=cfg.root)
-    output_repo_id, output_dir = get_output_path(
+    output_repo_id, output_dir, backup_path = get_output_path(
         cfg.repo_id,
         new_repo_id=cfg.new_repo_id,
         root=cfg.root,
@@ -533,8 +545,8 @@ def handle_remove_feature(cfg: EditDatasetConfig) -> None:
     )
 
     # In case of in-place modification, make the dataset point to the backup directory
-    if output_dir == dataset.root:
-        dataset.root = dataset.root.with_name(dataset.root.name + "_old")
+    if backup_path is not None:
+        dataset.root = backup_path
 
     logging.info(f"Removing features {cfg.operation.feature_names} from {cfg.repo_id}")
     new_dataset = remove_feature(
@@ -671,7 +683,7 @@ def handle_recompute_stats(cfg: EditDatasetConfig) -> None:
         cfg.new_root,
         default_new_repo_id=f"{cfg.repo_id}_recomputed_stats",
     )
-    in_place = output_root == input_root
+    in_place = _is_in_place(input_root, output_root)
 
     if in_place and not cfg.operation.overwrite:
         raise ValueError(
@@ -731,7 +743,7 @@ def handle_reencode_videos(cfg: EditDatasetConfig) -> None:
         cfg.new_root,
         default_new_repo_id=f"{cfg.repo_id}_reencoded",
     )
-    in_place = output_root == input_root
+    in_place = _is_in_place(input_root, output_root)
 
     if in_place and not cfg.operation.overwrite:
         raise ValueError(

src/lerobot/scripts/lerobot_train.py

@@ -243,9 +243,17 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         # Accelerate auto-detects the device based on the available hardware and ignores the policy.device setting.
         # Force the device to be CPU when the active config's device is set to CPU (works for both policy and reward model training).
         force_cpu = cfg.trainable_config.device == "cpu"
-        # Drive Accelerate's autocast from policy.dtype (bf16/fp16 activate it; float32/absent -> launcher default).
+        # Drive Accelerate's autocast from policy.dtype (bf16/fp16 activate it; float32 -> full precision).
+        has_policy_dtype = hasattr(cfg.trainable_config, "dtype")
         policy_dtype = getattr(cfg.trainable_config, "dtype", None)
         mixed_precision = {"bfloat16": "bf16", "float16": "fp16", "float32": "no"}.get(policy_dtype)
+        # Policies without a `dtype` field fall back to `use_amp`, which would otherwise be
+        # silently ignored here while lerobot-eval honors it. Follow torch.autocast's default
+        # for the configured device so training and evaluation use the same precision.
+        if not has_policy_dtype and getattr(cfg.trainable_config, "use_amp", False):
+            device_type = torch.device(cfg.trainable_config.device).type
+            autocast_dtype = torch.get_autocast_dtype(device_type)
+            mixed_precision = {torch.bfloat16: "bf16", torch.float16: "fp16"}[autocast_dtype]
         accelerator = Accelerator(
             step_scheduler_with_optimizer=False,
             mixed_precision=mixed_precision,

tests/robots/test_robot_kinematic_processor.py

@@ -17,9 +17,14 @@
 import pytest
 
 from lerobot.configs import FeatureType, PipelineFeatureType, PolicyFeature
+from lerobot.processor.converters import create_transition
 from lerobot.robots.so_follower.robot_kinematic_processor import (
+    EEReferenceAndDelta,
     ForwardKinematicsJointsToEEAction,
     ForwardKinematicsJointsToEEObservation,
+    GripperVelocityToJoint,
+    InverseKinematicsEEToJoints,
+    InverseKinematicsRLStep,
 )
 
 MOTOR_NAMES = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"]
@@ -43,3 +48,38 @@ def test_fk_feature_schema(step_cls, bucket, feature_type):
     out = step_cls(kinematics=None, motor_names=MOTOR_NAMES).transform_features(features)[bucket]
     assert set(out) == EE_KEYS
     assert {feature.type for feature in out.values()} == {feature_type}
+
+
+EE_ACTION = dict.fromkeys(EE_KEYS, 0.0)
+
+
+@pytest.mark.parametrize(
+    ("step", "action"),
+    [
+        (
+            EEReferenceAndDelta(kinematics=None, end_effector_step_sizes={}, motor_names=MOTOR_NAMES),
+            dict(EE_ACTION),
+        ),
+        (InverseKinematicsEEToJoints(kinematics=None, motor_names=MOTOR_NAMES), dict(EE_ACTION)),
+        (GripperVelocityToJoint(), {**EE_ACTION, "ee.gripper_vel": 0.0}),
+        (InverseKinematicsRLStep(kinematics=None, motor_names=MOTOR_NAMES), dict(EE_ACTION)),
+    ],
+    ids=[
+        "ee_reference_and_delta",
+        "inverse_kinematics_ee_to_joints",
+        "gripper_velocity_to_joint",
+        "inverse_kinematics_rl_step",
+    ],
+)
+def test_missing_observation_raises_value_error(step, action):
+    """A transition without an observation must surface the documented ValueError.
+
+    `RobotProcessorPipeline.process_action` builds its transition with
+    `create_transition(action=...)`, which sets `TransitionKey.OBSERVATION` to None.
+    These steps used to call `.copy()` on that before the None check, so the guard
+    below them was unreachable and an AttributeError escaped instead.
+    """
+    transition = create_transition(action=action)
+
+    with pytest.raises(ValueError, match="Joints observation"):
+        step(transition)

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "(python_full_version >= '3.15' and platform_machine == 'AMD64' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'x86_64' and sys_platform == 'linux')",
@@ -402,10 +402,10 @@ name = "bddl"
 version = "1.0.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "jupytext", marker = "sys_platform == 'linux'" },
-    { name = "networkx", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "pytest", marker = "sys_platform == 'linux'" },
+    { name = "jupytext" },
+    { name = "networkx" },
+    { name = "numpy" },
+    { name = "pytest" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5c/37/0211f82891a9f14efcfd2b2096f8d9e4351398ad637fdd1ee59cfc580b0e/bddl-1.0.1.tar.gz", hash = "sha256:1fa4e6e5050b93888ff6fd8455c39bfb29d3864ce06b4c37c0f781f513a2ae26", size = 164809, upload-time = "2022-03-08T01:48:23.564Z" }
 
@@ -1010,7 +1010,7 @@ name = "cuda-bindings"
 version = "12.9.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "sys_platform == 'linux'" },
+    { name = "cuda-pathfinder" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/32/45/557d4ed1fa54f0c7db8aee083229f624990d69f7d00f55477eed5c7e169a/cuda_bindings-12.9.7-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:0666d3c082ef8f4b2d670950589373550e9f3bf564d635dd883f24a0b40402ff", size = 7071026, upload-time = "2026-05-27T18:44:13.356Z" },
@@ -1043,37 +1043,37 @@ wheels = [
 
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cublas-cu12" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime-cu12" },
 ]
 cufft = [
-    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufft-cu12" },
 ]
 cufile = [
-    { name = "nvidia-cufile-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cufile-cu12" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti-cu12" },
 ]
 curand = [
-    { name = "nvidia-curand-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-curand-cu12" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusolver-cu12" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cusparse-cu12" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvrtc-cu12" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvtx-cu12" },
 ]
 
 [[package]]
@@ -1145,7 +1145,7 @@ name = "decord"
 version = "0.6.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "(platform_machine != 'arm64' and sys_platform == 'darwin') or (platform_machine == 'AMD64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "numpy" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/11/79/936af42edf90a7bd4e41a6cac89c913d4b47fa48a26b042d5129a9242ee3/decord-0.6.0-py3-none-manylinux2010_x86_64.whl", hash = "sha256:51997f20be8958e23b7c4061ba45d0efcd86bffd5fe81c695d0befee0d442976", size = 13602299, upload-time = "2021-06-14T21:30:55.486Z" },
@@ -1283,10 +1283,10 @@ resolution-markers = [
     "python_full_version == '3.14.*' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py", marker = "python_full_version >= '3.14'" },
-    { name = "attrs", marker = "python_full_version >= '3.14'" },
-    { name = "numpy", marker = "python_full_version >= '3.14'" },
-    { name = "wrapt", marker = "python_full_version >= '3.14'" },
+    { name = "absl-py" },
+    { name = "attrs" },
+    { name = "numpy" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a6/83/ce29720ccf934c6cfa9b9c95ebbe96558386e66886626066632b5e44afed/dm_tree-0.1.9.tar.gz", hash = "sha256:a4c7db3d3935a5a2d5e4b383fc26c6b0cd6f78c6d4605d3e7b518800ecd5342b", size = 35623, upload-time = "2025-01-30T20:45:37.13Z" }
 wheels = [
@@ -1324,10 +1324,10 @@ resolution-markers = [
     "python_full_version < '3.13' and sys_platform == 'win32'",
 ]
 dependencies = [
-    { name = "absl-py", marker = "python_full_version < '3.14'" },
-    { name = "attrs", marker = "python_full_version < '3.14'" },
-    { name = "numpy", marker = "python_full_version < '3.14'" },
-    { name = "wrapt", marker = "python_full_version < '3.14'" },
+    { name = "absl-py" },
+    { name = "attrs" },
+    { name = "numpy" },
+    { name = "wrapt" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/66/a3ec619d22b6baffa5ab853e8dc6ec9d0c837127948af59bb15b988d7312/dm_tree-0.1.10.tar.gz", hash = "sha256:22f37b599e01cc3402a17f79c257a802aebd8d326de05b54657650845956208a", size = 35748, upload-time = "2026-03-31T17:35:39.03Z" }
 wheels = [
@@ -1911,7 +1911,7 @@ name = "h5py"
 version = "3.16.0"
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