fix(aggregate stats): fix episode statistics aggregation. Frames and episodes index statistics are offseted and tastk index statistics recomputed from the new labels.

.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:

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/datasets/aggregate.py

@@ -22,6 +22,7 @@ from pathlib import Path
 from typing import Any, NotRequired, TypedDict
 
 import datasets
+import numpy as np
 import pandas as pd
 import tqdm
 
@@ -303,6 +304,46 @@ def update_meta_data(
     df["dataset_to_index"] = df["dataset_to_index"] + dst_meta.info.total_frames
     df["episode_index"] = df["episode_index"] + dst_meta.info.total_episodes
 
+    # Per-episode stats still describe the pre-merge values of the bookkeeping columns
+    # reindexed above. index/episode_index shift by a constant; task_index is relabeled,
+    # so recompute it from the episode's (stable) task strings via the unified tasks table.
+    shift_stat_keys = ("min", "max", "mean", "q01", "q10", "q50", "q90", "q99")
+    for name, offset in (
+        ("episode_index", dst_meta.info.total_episodes),
+        ("index", dst_meta.info.total_frames),
+    ):
+        for stat in shift_stat_keys:
+            col = f"stats/{name}/{stat}"
+            if col in df.columns:
+                df[col] = df[col] + offset
+
+    if any(c.startswith("stats/task_index/") for c in df.columns):
+        quantiles = {"q01": 0.01, "q10": 0.10, "q50": 0.50, "q90": 0.90, "q99": 0.99}
+        ids_per_row = [
+            np.array([dst_meta.tasks.loc[t, "task_index"] for t in tasks], dtype=np.float64)
+            for tasks in df["tasks"]
+        ]
+
+        def _task_stat(ids, stat):
+            if stat == "min":
+                return ids.min()
+            if stat == "max":
+                return ids.max()
+            if stat == "std":
+                return ids.std()
+            if stat in quantiles:
+                return np.quantile(ids, quantiles[stat])
+            return ids.mean()
+
+        for stat in ("min", "max", "mean", "std", *quantiles):
+            col = f"stats/task_index/{stat}"
+            if col in df.columns:
+                # np.full_like preserves each cell container and dtype so the parquet schema is unchanged.
+                df[col] = [
+                    np.full_like(orig, _task_stat(ids, stat))
+                    for orig, ids in zip(df[col], ids_per_row, strict=True)
+                ]
+
     return df
 
 

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."""

tests/datasets/test_aggregate.py

@@ -23,12 +23,15 @@ import pytest
 pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
 
 import datasets  # noqa: E402
+import numpy as np
+import pandas as pd
 import torch
 
 from lerobot.configs import VIDEO_ENCODER_INFO_KEYS
 from lerobot.datasets.aggregate import aggregate_datasets
 from lerobot.datasets.feature_utils import features_equal_for_merge
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.datasets.utils import EPISODES_DIR
 from tests.fixtures.constants import (
     DUMMY_CAMERA_FEATURES_WITH_DEPTH,
     DUMMY_REPO_ID,
@@ -857,3 +860,65 @@ def test_aggregate_already_merged_dataset(tmp_path, lerobot_dataset_factory):
 
     # This would raise FileNotFoundError before the fix
     assert_dataset_iteration_works(ds_abc)
+
+
+def test_aggregate_updates_per_episode_stats(tmp_path):
+    """episode_index/index/task_index per-episode stats follow the merge; all other stats are copied verbatim."""
+    features = {"observation.state": {"dtype": "float32", "shape": (2,), "names": None}}
+
+    def _make_dataset(suffix, tasks):
+        ds = LeRobotDataset.create(
+            f"{DUMMY_REPO_ID}_{suffix}", fps=10, features=features, root=tmp_path / suffix
+        )
+        for task in tasks:
+            for _ in range(4):
+                ds.add_frame({"observation.state": torch.randn(2), "task": task})
+            ds.save_episode()
+        ds.finalize()
+        return ds
+
+    # Overlapping tasks so relabeling collapses shared "b" and introduces new "c".
+    sources = [_make_dataset("s0", ["a", "b"]), _make_dataset("s1", ["b", "c"])]
+    aggr_root = tmp_path / "aggr"
+    aggregate_datasets(
+        repo_ids=[d.repo_id for d in sources],
+        roots=[d.root for d in sources],
+        aggr_repo_id=f"{DUMMY_REPO_ID}_aggr",
+        aggr_root=aggr_root,
+    )
+    with (
+        patch("lerobot.datasets.dataset_metadata.get_safe_version", return_value="v3.0"),
+        patch("lerobot.datasets.dataset_metadata.snapshot_download", return_value=str(aggr_root)),
+    ):
+        aggr = LeRobotDataset(f"{DUMMY_REPO_ID}_aggr", root=aggr_root)
+    assert aggr.meta.total_tasks == 3  # "b" deduped, "c" added
+
+    def _load_stats(root):
+        # load_episodes drops stats/* columns, so read the episodes parquet shards directly.
+        shards = sorted((root / EPISODES_DIR).rglob("*.parquet"))
+        return pd.concat([pd.read_parquet(s) for s in shards]).set_index("episode_index")
+
+    merged = _load_stats(aggr_root)
+    src_rows, ep_off, fr_off = [], 0, 0
+    for d in sources:
+        src = _load_stats(d.root)
+        src_rows += [(src.loc[ep], ep_off, fr_off) for ep in range(d.num_episodes)]
+        ep_off, fr_off = ep_off + d.num_episodes, fr_off + d.num_frames
+
+    shift = {"min", "max", "mean", "q01", "q10", "q50", "q90", "q99"}
+    for ep, (src_row, e_off, f_off) in enumerate(src_rows):
+        row = merged.loc[ep]
+        new_id = float(aggr.meta.tasks.loc[row["tasks"][0], "task_index"])
+        for col in (c for c in merged.columns if c.startswith("stats/")):
+            feat, key = col[len("stats/") :].rsplit("/", 1)
+            got = np.asarray(row[col], dtype=np.float64).reshape(-1)
+            base = np.asarray(src_row[col], dtype=np.float64).reshape(-1)
+            if feat == "episode_index":
+                expected = base + e_off if key in shift else base
+            elif feat == "index":
+                expected = base + f_off if key in shift else base
+            elif feat == "task_index":
+                expected = base if key == "count" else np.full_like(base, 0.0 if key == "std" else new_id)
+            else:
+                expected = base
+            assert np.allclose(got, expected), f"ep{ep} {col}: {got} != {expected}"

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"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/db/33/acd0ce6863b6c0d7735007df01815403f5589a21ff8c2e1ee2587a38f548/h5py-3.16.0.tar.gz", hash = "sha256:a0dbaad796840ccaa67a4c144a0d0c8080073c34c76d5a6941d6818678ef2738", size = 446526, upload-time = "2026-03-06T13:49:08.07Z" }
 wheels = [
@@ -1955,23 +1955,23 @@ name = "hf-libero"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "bddl", marker = "sys_platform == 'linux'" },
-    { name = "cloudpickle", marker = "sys_platform == 'linux'" },
-    { name = "easydict", marker = "sys_platform == 'linux'" },
-    { name = "einops", marker = "sys_platform == 'linux'" },
-    { name = "future", marker = "sys_platform == 'linux'" },
-    { name = "gymnasium", marker = "sys_platform == 'linux'" },
-    { name = "hf-egl-probe", marker = "sys_platform == 'linux'" },
-    { name = "hydra-core", marker = "sys_platform == 'linux'" },
-    { name = "matplotlib", marker = "sys_platform == 'linux'" },
-    { name = "mujoco", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
-    { name = "opencv-python", marker = "sys_platform == 'linux'" },
-    { name = "robomimic", marker = "sys_platform == 'linux'" },
-    { name = "robosuite", marker = "sys_platform == 'linux'" },
-    { name = "thop", marker = "sys_platform == 'linux'" },
-    { name = "transformers", marker = "sys_platform == 'linux'" },
-    { name = "wandb", marker = "sys_platform == 'linux'" },
+    { name = "bddl" },
+    { name = "cloudpickle" },
+    { name = "easydict" },
+    { name = "einops" },
+    { name = "future" },
+    { name = "gymnasium" },
+    { name = "hf-egl-probe" },
+    { name = "hydra-core" },
+    { name = "matplotlib" },
+    { name = "mujoco" },
+    { name = "numpy" },
+    { name = "opencv-python" },
+    { name = "robomimic" },
+    { name = "robosuite" },
+    { name = "thop" },
+    { name = "transformers" },
+    { name = "wandb" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/af/aa/4e9eb8715e0bff9cb6553db563a35d253393097d446f82bd53575e8b253d/hf_libero-0.1.4.tar.gz", hash = "sha256:c058d67ad5a2b589529c14d614282ef4cca3a7763dafa134f58a6c9039657e34", size = 2961319, upload-time = "2026-06-10T09:56:13.994Z" }
 wheels = [
@@ -2122,9 +2122,9 @@ name = "hydra-core"
 version = "1.3.4"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "antlr4-python3-runtime", marker = "sys_platform == 'linux'" },
-    { name = "omegaconf", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
+    { name = "antlr4-python3-runtime" },
+    { name = "omegaconf" },
+    { name = "packaging" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/10/dd/220f0e91743136725352497e98540772a01fc7c3ab96ff16c3c74424e984/hydra_core-1.3.4.tar.gz", hash = "sha256:ad0f7b05a0242255a8984d5a4ed2f6847f7b783ed727368a2c0155ec52d6c34c", size = 3263348, upload-time = "2026-07-04T16:25:38.891Z" }
 wheels = [
@@ -2677,11 +2677,11 @@ name = "jupytext"
 version = "1.19.5"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
-    { name = "mdit-py-plugins", marker = "sys_platform == 'linux'" },
-    { name = "nbformat", marker = "sys_platform == 'linux'" },
-    { name = "packaging", marker = "sys_platform == 'linux'" },
-    { name = "pyyaml", marker = "sys_platform == 'linux'" },
+    { name = "markdown-it-py" },
+    { name = "mdit-py-plugins" },
+    { name = "nbformat" },
+    { name = "packaging" },
+    { name = "pyyaml" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/a1/ca/473f8ebb101553fb2ea6ab1d34324d6677844c968947ac050c759d539f2c/jupytext-1.19.5.tar.gz", hash = "sha256:605026446d605aa54fd7f7fc69df6ae51c7a46053d4cebf05afdc64d66de3df0", size = 4600916, upload-time = "2026-07-21T22:00:29.198Z" }
 wheels = [
@@ -3471,7 +3471,7 @@ requires-dist = [
     { name = "pyrealsense2", marker = "sys_platform != 'darwin' and extra == 'intelrealsense'", specifier = ">=2.55.1.6486,<2.57.0" },
     { name = "pyrealsense2-macosx", marker = "sys_platform == 'darwin' and extra == 'intelrealsense'", specifier = ">=2.54,<2.57.0" },
     { name = "pyserial", marker = "extra == 'pyserial-dep'", specifier = ">=3.5,<4.0" },
-    { name = "pytest", marker = "extra == 'test'", specifier = ">=8.1.0,<9.0.0" },
+    { name = "pytest", marker = "extra == 'test'", specifier = ">=8.1.0,<10.0.0" },
     { name = "pytest-cov", marker = "extra == 'test'", specifier = ">=5.0.0,<8.0.0" },
     { name = "pytest-timeout", marker = "extra == 'test'", specifier = ">=2.4.0,<3.0.0" },
     { name = "python-can", marker = "extra == 'can-dep'", specifier = ">=4.2.0,<5.0.0" },
@@ -3485,7 +3485,7 @@ requires-dist = [
     { name = "scikit-image", marker = "extra == 'video-benchmark'", specifier = ">=0.23.2,<0.26.0" },
     { name = "scipy", marker = "extra == 'all'", specifier = ">=1.14.0,<2.0.0" },
     { name = "scipy", marker = "extra == 'scipy-dep'", specifier = ">=1.14.0,<2.0.0" },
-    { name = "setuptools", specifier = ">=71.0.0,<81.0.0" },
+    { name = "setuptools", specifier = ">=71.0.0,<82.0.0" },
     { name = "teleop", marker = "extra == 'phone'", specifier = ">=0.1.0,<0.2.0" },
     { name = "termcolor", specifier = ">=2.4.0,<4.0.0" },
     { name = "timm", marker = "extra == 'timm-dep'", specifier = ">=1.0.0,<1.1.0" },
@@ -3816,7 +3816,7 @@ name = "mdit-py-plugins"
 version = "0.6.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "markdown-it-py", marker = "sys_platform == 'linux'" },
+    { name = "markdown-it-py" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/59/fc/f8d0863f8862f25602c0404d75568e89fb6b4109804645e5cdfb1be5cf56/mdit_py_plugins-0.6.1.tar.gz", hash = "sha256:a2bca0f039f39dbd35fb74ae1b5f998608c437463371f0ff7f49a19a17a114d0", size = 56114, upload-time = "2026-05-13T09:03:38.91Z" }
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@@ -4295,8 +4295,8 @@ name = "numba"
 version = "0.66.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "llvmlite", marker = "sys_platform == 'linux'" },
-    { name = "numpy", marker = "sys_platform == 'linux'" },
+    { name = "llvmlite" },
+    { name = "numpy" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ae/a0/570e3dc53e5602b49108f62a13e529f1eec8bfc7ef37d49c825924dcf546/numba-0.66.0.tar.gz", hash = "sha256:b900e63a0e26c05ea9a6d5a3a5a0a177cb64c5011887bf43edb8c3ed2c38d363", size = 2806181, upload-time = "2026-07-01T23:12:46.36Z" }
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@@ -4389,7 +4389,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.19.0.56"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cublas-cu12" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/09/b8/277c51962ee46fa3e5b203ac5f76107c650f781d6891e681e28e6f3e9fe6/nvidia_cudnn_cu12-9.19.0.56-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:08caaf27fe556aca82a3ee3b5aa49a77e7de0cfcb7ff4e5c29da426387a8267e", size = 656910700, upload-time = "2026-02-03T20:40:25.508Z" },
@@ -4401,7 +4401,7 @@ name = "nvidia-cufft-cu12"
 version = "11.3.3.83"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/60/bc/7771846d3a0272026c416fbb7e5f4c1f146d6d80704534d0b187dd6f4800/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a", size = 193109211, upload-time = "2025-03-07T01:44:56.873Z" },
@@ -4431,9 +4431,9 @@ name = "nvidia-cusolver-cu12"
 version = "11.7.3.90"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux'" },
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cusparse-cu12" },
+    { name = "nvidia-nvjitlink-cu12" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/c8/32/f7cd6ce8a7690544d084ea21c26e910a97e077c9b7f07bf5de623ee19981/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0", size = 267229841, upload-time = "2025-03-07T01:46:54.356Z" },
@@ -4445,7 +4445,7 @@ name = "nvidia-cusparse-cu12"
 version = "12.5.8.93"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink-cu12" },
 ]
 wheels = [
     { url = "https://files.pythonhosted.org/packages/bc/f7/cd777c4109681367721b00a106f491e0d0d15cfa1fd59672ce580ce42a97/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc", size = 288117129, upload-time = "2025-03-07T01:47:40.407Z" },
@@ -4502,8 +4502,8 @@ name = "omegaconf"
 version = "2.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "antlr4-python3-runtime", marker = "sys_platform == 'linux'" },
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