feat(sim): add metaworld env (#2088)

* add metaworld

* smol update

Signed-off-by: Jade Choghari <chogharijade@gmail.com>

* update design

* Update src/lerobot/envs/metaworld.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Signed-off-by: Jade Choghari <chogharijade@gmail.com>

* update

* small changes

* iterate on review

* small fix

* small fix

* add docs

* update doc

* add better gif

* smol doc fix

* updage gymnasium

* add note

* depreciate gym-xarm

* more changes

* update doc

* comply with mypy

* more fixes

* update readme

* precommit

* update pusht

* add pusht instead

* changes

* style

* add changes

* update

* revert

* update v2

* chore(envs): move metaworld config to its own file + remove comments + simplify _format_raw_obs (#2200)

* update final changes

---------

Signed-off-by: Jade Choghari <chogharijade@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>

CONTRIBUTING.md

@@ -72,7 +72,6 @@ post it.
 
 Look at our implementations for [datasets](./src/lerobot/datasets/), [policies](./src/lerobot/policies/),
 environments ([aloha](https://github.com/huggingface/gym-aloha),
-[xarm](https://github.com/huggingface/gym-xarm),
 [pusht](https://github.com/huggingface/gym-pusht))
 and follow the same api design.
 

Makefile

@@ -119,10 +119,9 @@ test-tdmpc-ete-train:
 		--policy.type=tdmpc \
 		--policy.device=$(DEVICE) \
 		--policy.push_to_hub=false \
-		--env.type=xarm \
+		--env.type=pusht \
-		--env.task=XarmLift-v0 \
 		--env.episode_length=5 \
-		--dataset.repo_id=lerobot/xarm_lift_medium \
+		--dataset.repo_id=lerobot/pusht_image \
 		--dataset.image_transforms.enable=true \
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
@@ -140,9 +139,10 @@ test-tdmpc-ete-eval:
 	lerobot-eval \
 		--policy.path=tests/outputs/tdmpc/checkpoints/000002/pretrained_model \
 		--policy.device=$(DEVICE) \
-		--env.type=xarm \
+		--env.type=pusht \
 		--env.episode_length=5 \
-		--env.task=XarmLift-v0 \
+		--env.observation_height=96 \
+        --env.observation_width=96 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1
 

docs/source/_toctree.yml

@@ -7,8 +7,6 @@
 - sections:
   - local: il_robots
     title: Imitation Learning for Robots
-  - local: il_sim
-    title: Imitation Learning in Sim
   - local: cameras
     title: Cameras
   - local: integrate_hardware
@@ -37,9 +35,15 @@
     title: π₀ (Pi0)
   - local: pi05
     title: π₀.₅ (Pi05)
+  title: "Policies"
+- sections:
+  - local: il_sim
+    title: Imitation Learning in Sim
   - local: libero
     title: Using Libero
-  title: "Policies"
+  - local: metaworld
+    title: Using MetaWorld
+  title: "Simulation"
 - sections:
   - local: introduction_processors
     title: Introduction to Robot Processors

docs/source/installation.mdx

@@ -91,7 +91,7 @@ LeRobot provides optional extras for specific functionalities. Multiple extras c
 
 ### Simulations
 
-Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), `xarm` ([gym-xarm](https://github.com/huggingface/gym-xarm)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
+Install environment packages: `aloha` ([gym-aloha](https://github.com/huggingface/gym-aloha)), or `pusht` ([gym-pusht](https://github.com/huggingface/gym-pusht))
 Example:
 
 ```bash

docs/source/libero.mdx

@@ -137,7 +137,7 @@ The finetuned model can be found here:
 We then evaluate the finetuned model using the LeRobot LIBERO implementation, by running the following command:
 
 ```bash
-python src/lerobot/scripts/eval.py \
+lerobot-eval \
   --output_dir=/logs/ \
   --env.type=libero \
   --env.task=libero_spatial,libero_object,libero_goal,libero_10 \

docs/source/metaworld.mdx

@@ -0,0 +1,80 @@
+# Meta-World
+
+Meta-World is a well-designed, open-source simulation benchmark for multi-task and meta reinforcement learning in continuous-control robotic manipulation. It gives researchers a shared, realistic playground to test whether algorithms can _learn many different tasks_ and _generalize quickly to new ones_ — two central challenges for real-world robotics.
+
+- 📄 [MetaWorld paper](https://arxiv.org/pdf/1910.10897)
+- 💻 [Original MetaWorld repo](https://github.com/Farama-Foundation/Metaworld)
+
+![MetaWorld MT10 demo](https://meta-world.github.io/figures/ml45.gif)
+
+## Why Meta-World matters
+
+- **Diverse, realistic tasks.** Meta-World bundles a large suite of simulated manipulation tasks (50 in the MT50 suite) using everyday objects and a common tabletop Sawyer arm. This diversity exposes algorithms to a wide variety of dynamics, contacts and goal specifications while keeping a consistent control and observation structure.
+- **Focus on generalization and multi-task learning.** By evaluating across task distributions that share structure but differ in goals and objects, Meta-World reveals whether an agent truly learns transferable skills rather than overfitting to a narrow task.
+- **Standardized evaluation protocol.** It provides clear evaluation modes and difficulty splits, so different methods can be compared fairly across easy, medium, hard and very-hard regimes.
+- **Empirical insight.** Past evaluations on Meta-World show impressive progress on some fronts, but also highlight that current multi-task and meta-RL methods still struggle with large, diverse task sets. That gap points to important research directions.
+
+## What it enables in LeRobot
+
+In LeRobot, you can evaluate any policy or vision-language-action (VLA) model on Meta-World tasks and get a clear success-rate measure. The integration is designed to be straightforward:
+
+- We provide a LeRobot-ready dataset for Meta-World (MT50) on the HF Hub: `https://huggingface.co/datasets/lerobot/metaworld_mt50`.
+  - This dataset is formatted for the MT50 evaluation that uses all 50 tasks (the most challenging multi-task setting).
+  - MT50 gives the policy a one-hot task vector and uses fixed object/goal positions for consistency.
+
+- Task descriptions and the exact keys required for evaluation are available in the repo/dataset — use these to ensure your policy outputs the right success signals.
+
+## Quick start, train a SmolVLA policy on Meta-World
+
+Example command to train a SmolVLA policy on a subset of tasks:
+
+```bash
+lerobot-train \
+  --policy.type=smolvla \
+  --policy.repo_id=${HF_USER}/metaworld-test \
+  --policy.load_vlm_weights=true \
+  --dataset.repo_id=lerobot/metaworld_mt50 \
+  --env.type=metaworld \
+  --env.task=assembly-v3,dial-turn-v3,handle-press-side-v3 \
+  --output_dir=./outputs/ \
+  --steps=100000 \
+  --batch_size=4 \
+  --eval.batch_size=1 \
+  --eval.n_episodes=1 \
+  --eval_freq=1000
+```
+
+Notes:
+
+- `--env.task` accepts explicit task lists (comma separated) or difficulty groups (e.g., `env.task="hard"`).
+- Adjust `batch_size`, `steps`, and `eval_freq` to match your compute budget.
+- **Gymnasium Assertion Error**: if you encounter an error like
+  `AssertionError: ['human', 'rgb_array', 'depth_array']` when running MetaWorld environments, this comes from a mismatch between MetaWorld and your Gymnasium version.
+  We recommend using:
+
+```bash
+  pip install "gymnasium==1.1.0"
+```
+
+to ensure proper compatibility.
+
+## Quick start — evaluate a trained policy
+
+To evaluate a trained policy on the Meta-World medium difficulty split:
+
+```bash
+lerobot-eval \
+  --policy.path="your-policy-id" \
+  --env.type=metaworld \
+  --env.task=medium \
+  --eval.batch_size=1 \
+  --eval.n_episodes=2
+```
+
+This will run episodes and return per-task success rates using the standard Meta-World evaluation keys.
+
+## Practical tips
+
+- If you care about generalization, run on the full MT50 suite — it’s intentionally challenging and reveals strengths/weaknesses better than a few narrow tasks.
+- Use the one-hot task conditioning for multi-task training (MT10 / MT50 conventions) so policies have explicit task context.
+- Inspect the dataset task descriptions and the `info["is_success"]` keys when writing post-processing or logging so your success metrics line up with the benchmark.

pyproject.toml

@@ -80,7 +80,7 @@ dependencies = [
     "torchvision>=0.21.0,<0.23.0", # TODO: Bumb dependency
 
     "draccus==0.10.0", # TODO: Remove ==
-    "gymnasium>=0.29.1,<1.0.0", # TODO: Bumb dependency
+    "gymnasium>=1.0.0",
     "rerun-sdk>=0.21.0,<0.23.0", # TODO: Bumb dependency
 
     # Support dependencies
@@ -133,11 +133,10 @@ test = ["pytest>=8.1.0,<9.0.0", "pytest-timeout>=2.4.0,<3.0.0", "pytest-cov>=5.0
 video_benchmark = ["scikit-image>=0.23.2,<0.26.0", "pandas>=2.2.2,<2.4.0"]
 
 # Simulation
-aloha = ["gym-aloha>=0.1.1,<0.2.0"]
+aloha = ["gym-aloha>=0.1.2,<0.2.0"]
 pusht = ["gym-pusht>=0.1.5,<0.2.0", "pymunk>=6.6.0,<7.0.0"] # TODO: Fix pymunk version in gym-pusht instead
-xarm = ["gym-xarm>=0.1.1,<0.2.0"]
+libero = ["lerobot[transformers-dep]", "libero @ git+https://github.com/huggingface/lerobot-libero.git@upgrade-dep#egg=libero"]
-libero = ["lerobot[transformers-dep]", "libero @ git+https://github.com/huggingface/lerobot-libero.git@main#egg=libero"]
+metaworld = ["metaworld>=3.0.0"]
-
 
 # All
 all = [
@@ -157,9 +156,9 @@ all = [
     "lerobot[video_benchmark]",
     "lerobot[aloha]",
     "lerobot[pusht]",
-    "lerobot[xarm]",
     "lerobot[phone]",
     "lerobot[libero]",
+    "lerobot[metaworld]",
 ]
 
 [project.scripts]

src/lerobot/__init__.py

@@ -57,7 +57,6 @@ available_tasks_per_env = {
         "AlohaTransferCube-v0",
     ],
     "pusht": ["PushT-v0"],
-    "xarm": ["XarmLift-v0"],
 }
 available_envs = list(available_tasks_per_env.keys())
 
@@ -75,16 +74,6 @@ available_datasets_per_env = {
     # TODO(alexander-soare): Add "lerobot/pusht_keypoints". Right now we can't because this is too tightly
     # coupled with tests.
     "pusht": ["lerobot/pusht", "lerobot/pusht_image"],
-    "xarm": [
-        "lerobot/xarm_lift_medium",
-        "lerobot/xarm_lift_medium_replay",
-        "lerobot/xarm_push_medium",
-        "lerobot/xarm_push_medium_replay",
-        "lerobot/xarm_lift_medium_image",
-        "lerobot/xarm_lift_medium_replay_image",
-        "lerobot/xarm_push_medium_image",
-        "lerobot/xarm_push_medium_replay_image",
-    ],
 }
 
 available_real_world_datasets = [
@@ -195,7 +184,6 @@ available_motors = [
 available_policies_per_env = {
     "aloha": ["act"],
     "pusht": ["diffusion", "vqbet"],
-    "xarm": ["tdmpc"],
     "koch_real": ["act_koch_real"],
     "aloha_real": ["act_aloha_real"],
 }

src/lerobot/envs/__init__.py

@@ -12,4 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .configs import AlohaEnv, EnvConfig, PushtEnv, XarmEnv  # noqa: F401
+from .configs import AlohaEnv, EnvConfig, PushtEnv  # noqa: F401

src/lerobot/envs/configs.py

@@ -133,45 +133,6 @@ class PushtEnv(EnvConfig):
         }
 
 
-@EnvConfig.register_subclass("xarm")
-@dataclass
-class XarmEnv(EnvConfig):
-    task: str | None = "XarmLift-v0"
-    fps: int = 15
-    episode_length: int = 200
-    obs_type: str = "pixels_agent_pos"
-    render_mode: str = "rgb_array"
-    visualization_width: int = 384
-    visualization_height: int = 384
-    features: dict[str, PolicyFeature] = field(
-        default_factory=lambda: {
-            ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(4,)),
-            "pixels": PolicyFeature(type=FeatureType.VISUAL, shape=(84, 84, 3)),
-        }
-    )
-    features_map: dict[str, str] = field(
-        default_factory=lambda: {
-            ACTION: ACTION,
-            "agent_pos": OBS_STATE,
-            "pixels": OBS_IMAGE,
-        }
-    )
-
-    def __post_init__(self):
-        if self.obs_type == "pixels_agent_pos":
-            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(4,))
-
-    @property
-    def gym_kwargs(self) -> dict:
-        return {
-            "obs_type": self.obs_type,
-            "render_mode": self.render_mode,
-            "visualization_width": self.visualization_width,
-            "visualization_height": self.visualization_height,
-            "max_episode_steps": self.episode_length,
-        }
-
-
 @dataclass
 class ImagePreprocessingConfig:
     crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
@@ -306,3 +267,45 @@ class LiberoEnv(EnvConfig):
             "obs_type": self.obs_type,
             "render_mode": self.render_mode,
         }
+
+
+@EnvConfig.register_subclass("metaworld")
+@dataclass
+class MetaworldEnv(EnvConfig):
+    task: str = "metaworld-push-v2"  # add all tasks
+    fps: int = 80
+    episode_length: int = 400
+    obs_type: str = "pixels_agent_pos"
+    render_mode: str = "rgb_array"
+    multitask_eval: bool = True
+    features: dict[str, PolicyFeature] = field(
+        default_factory=lambda: {
+            "action": PolicyFeature(type=FeatureType.ACTION, shape=(4,)),
+        }
+    )
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            "action": ACTION,
+            "agent_pos": OBS_STATE,
+            "top": f"{OBS_IMAGE}",
+            "pixels/top": f"{OBS_IMAGE}",
+        }
+    )
+
+    def __post_init__(self):
+        if self.obs_type == "pixels":
+            self.features["top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 480, 3))
+
+        elif self.obs_type == "pixels_agent_pos":
+            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(4,))
+            self.features["pixels/top"] = PolicyFeature(type=FeatureType.VISUAL, shape=(480, 480, 3))
+
+        else:
+            raise ValueError(f"Unsupported obs_type: {self.obs_type}")
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {
+            "obs_type": self.obs_type,
+            "render_mode": self.render_mode,
+        }

src/lerobot/envs/factory.py

@@ -17,7 +17,7 @@ import importlib
 
 import gymnasium as gym
 
-from lerobot.envs.configs import AlohaEnv, EnvConfig, LiberoEnv, PushtEnv, XarmEnv
+from lerobot.envs.configs import AlohaEnv, EnvConfig, LiberoEnv, PushtEnv
 
 
 def make_env_config(env_type: str, **kwargs) -> EnvConfig:
@@ -25,8 +25,6 @@ def make_env_config(env_type: str, **kwargs) -> EnvConfig:
         return AlohaEnv(**kwargs)
     elif env_type == "pusht":
         return PushtEnv(**kwargs)
-    elif env_type == "xarm":
-        return XarmEnv(**kwargs)
     elif env_type == "libero":
         return LiberoEnv(**kwargs)
     else:
@@ -74,7 +72,18 @@ def make_env(
             gym_kwargs=cfg.gym_kwargs,
             env_cls=env_cls,
         )
+    elif "metaworld" in cfg.type:
+        from lerobot.envs.metaworld import create_metaworld_envs
 
+        if cfg.task is None:
+            raise ValueError("MetaWorld requires a task to be specified")
+
+        return create_metaworld_envs(
+            task=cfg.task,
+            n_envs=n_envs,
+            gym_kwargs=cfg.gym_kwargs,
+            env_cls=env_cls,
+        )
     package_name = f"gym_{cfg.type}"
     try:
         importlib.import_module(package_name)
@@ -87,7 +96,7 @@ def make_env(
     def _make_one():
         return gym.make(gym_handle, disable_env_checker=cfg.disable_env_checker, **(cfg.gym_kwargs or {}))
 
-    vec = env_cls([_make_one for _ in range(n_envs)])
+    vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=gym.vector.AutoresetMode.SAME_STEP)
 
     # normalize to {suite: {task_id: vec_env}} for consistency
     suite_name = cfg.type  # e.g., "pusht", "aloha"

src/lerobot/envs/libero.py

@@ -260,19 +260,23 @@ class LiberoEnv(gym.Env):
 
         is_success = self._env.check_success()
         terminated = done or is_success
-        info["is_success"] = is_success
+        info.update(
-
+            {
+                "task": self.task,
+                "task_id": self.task_id,
+                "done": done,
+                "is_success": is_success,
+            }
+        )
         observation = self._format_raw_obs(raw_obs)
-        if done:
+        if terminated:
+            info["final_info"] = {
+                "task": self.task,
+                "task_id": self.task_id,
+                "done": bool(done),
+                "is_success": bool(is_success),
+            }
             self.reset()
-            info.update(
-                {
-                    "task": self.task,
-                    "task_id": self.task_id,
-                    "done": done,
-                    "is_success": is_success,
-                }
-            )
         truncated = False
         return observation, reward, terminated, truncated, info
 

src/lerobot/envs/metaworld.py

@@ -0,0 +1,313 @@
+#!/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.
+import json
+from collections import defaultdict
+from collections.abc import Callable, Sequence
+from pathlib import Path
+from typing import Any
+
+import gymnasium as gym
+import metaworld
+import metaworld.policies as policies
+import numpy as np
+from gymnasium import spaces
+
+# ---- Load configuration data from the external JSON file ----
+CONFIG_PATH = Path(__file__).parent / "metaworld_config.json"
+try:
+    with open(CONFIG_PATH) as f:
+        data = json.load(f)
+except FileNotFoundError as err:
+    raise FileNotFoundError(
+        "Could not find 'metaworld_config.json'. "
+        "Please ensure the configuration file is in the same directory as the script."
+    ) from err
+except json.JSONDecodeError as err:
+    raise ValueError(
+        "Failed to decode 'metaworld_config.json'. Please ensure it is a valid JSON file."
+    ) from err
+
+# ---- Process the loaded data ----
+
+# extract and type-check top-level dicts
+task_descriptions_obj = data.get("TASK_DESCRIPTIONS")
+if not isinstance(task_descriptions_obj, dict):
+    raise TypeError("Expected TASK_DESCRIPTIONS to be a dict[str, str]")
+TASK_DESCRIPTIONS: dict[str, str] = task_descriptions_obj
+
+task_name_to_id_obj = data.get("TASK_NAME_TO_ID")
+if not isinstance(task_name_to_id_obj, dict):
+    raise TypeError("Expected TASK_NAME_TO_ID to be a dict[str, int]")
+TASK_NAME_TO_ID: dict[str, int] = task_name_to_id_obj
+
+# difficulty -> tasks mapping
+difficulty_to_tasks = data.get("DIFFICULTY_TO_TASKS")
+if not isinstance(difficulty_to_tasks, dict):
+    raise TypeError("Expected 'DIFFICULTY_TO_TASKS' to be a dict[str, list[str]]")
+DIFFICULTY_TO_TASKS: dict[str, list[str]] = difficulty_to_tasks
+
+# convert policy strings -> actual policy classes
+task_policy_mapping = data.get("TASK_POLICY_MAPPING")
+if not isinstance(task_policy_mapping, dict):
+    raise TypeError("Expected 'TASK_POLICY_MAPPING' to be a dict[str, str]")
+TASK_POLICY_MAPPING: dict[str, Any] = {
+    task_name: getattr(policies, policy_class_name)
+    for task_name, policy_class_name in task_policy_mapping.items()
+}
+ACTION_DIM = 4
+OBS_DIM = 4
+
+
+class MetaworldEnv(gym.Env):
+    metadata = {"render_modes": ["rgb_array"], "render_fps": 80}
+
+    def __init__(
+        self,
+        task,
+        camera_name="corner2",
+        obs_type="pixels",
+        render_mode="rgb_array",
+        observation_width=480,
+        observation_height=480,
+        visualization_width=640,
+        visualization_height=480,
+    ):
+        super().__init__()
+        self.task = task.replace("metaworld-", "")
+        self.obs_type = obs_type
+        self.render_mode = render_mode
+        self.observation_width = observation_width
+        self.observation_height = observation_height
+        self.visualization_width = visualization_width
+        self.visualization_height = visualization_height
+        self.camera_name = camera_name
+
+        self._env = self._make_envs_task(self.task)
+        self._max_episode_steps = self._env.max_path_length
+        self.task_description = TASK_DESCRIPTIONS[self.task]
+
+        self.expert_policy = TASK_POLICY_MAPPING[self.task]()
+
+        if self.obs_type == "state":
+            raise NotImplementedError()
+        elif self.obs_type == "pixels":
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Box(
+                        low=0,
+                        high=255,
+                        shape=(self.observation_height, self.observation_width, 3),
+                        dtype=np.uint8,
+                    )
+                }
+            )
+        elif self.obs_type == "pixels_agent_pos":
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Box(
+                        low=0,
+                        high=255,
+                        shape=(self.observation_height, self.observation_width, 3),
+                        dtype=np.uint8,
+                    ),
+                    "agent_pos": spaces.Box(
+                        low=-1000.0,
+                        high=1000.0,
+                        shape=(OBS_DIM,),
+                        dtype=np.float64,
+                    ),
+                }
+            )
+
+        self.action_space = spaces.Box(low=-1, high=1, shape=(ACTION_DIM,), dtype=np.float32)
+
+    def render(self) -> np.ndarray:
+        """
+        Render the current environment frame.
+
+        Returns:
+            np.ndarray: The rendered RGB image from the environment.
+        """
+        image = self._env.render()
+        if self.camera_name == "corner2":
+            # Images from this camera are flipped — correct them
+            image = np.flip(image, (0, 1))
+        return image
+
+    def _make_envs_task(self, env_name: str):
+        mt1 = metaworld.MT1(env_name, seed=42)
+        env = mt1.train_classes[env_name](render_mode="rgb_array", camera_name=self.camera_name)
+        env.set_task(mt1.train_tasks[0])
+        if self.camera_name == "corner2":
+            env.model.cam_pos[2] = [
+                0.75,
+                0.075,
+                0.7,
+            ]  # corner2 position, similar to https://arxiv.org/pdf/2206.14244
+        env.reset()
+        env._freeze_rand_vec = False  # otherwise no randomization
+        return env
+
+    def _format_raw_obs(self, raw_obs: np.ndarray) -> dict[str, Any]:
+        image = None
+        if self._env is not None:
+            image = self._env.render()
+            if self.camera_name == "corner2":
+                # NOTE: The "corner2" camera in MetaWorld environments outputs images with both axes inverted.
+                image = np.flip(image, (0, 1))
+        agent_pos = raw_obs[:4]
+        if self.obs_type == "state":
+            raise NotImplementedError(
+                "'state' obs_type not implemented for MetaWorld. Use pixel modes instead."
+            )
+
+        elif self.obs_type in ("pixels", "pixels_agent_pos"):
+            assert image is not None, (
+                "Expected `image` to be rendered before constructing pixel-based observations. "
+                "This likely means `env.render()` returned None or the environment was not provided."
+            )
+
+            if self.obs_type == "pixels":
+                obs = {"pixels": image.copy()}
+
+            else:  # pixels_agent_pos
+                obs = {
+                    "pixels": image.copy(),
+                    "agent_pos": agent_pos,
+                }
+        else:
+            raise ValueError(f"Unknown obs_type: {self.obs_type}")
+        return obs
+
+    def reset(
+        self,
+        seed: int | None = None,
+        **kwargs,
+    ) -> tuple[dict[str, Any], dict[str, Any]]:
+        """
+        Reset the environment to its initial state.
+
+        Args:
+            seed (Optional[int]): Random seed for environment initialization.
+
+        Returns:
+            observation (Dict[str, Any]): The initial formatted observation.
+            info (Dict[str, Any]): Additional info about the reset state.
+        """
+        super().reset(seed=seed)
+
+        raw_obs, info = self._env.reset(seed=seed)
+
+        observation = self._format_raw_obs(raw_obs)
+
+        info = {"is_success": False}
+        return observation, info
+
+    def step(self, action: np.ndarray) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
+        """
+        Perform one environment step.
+
+        Args:
+            action (np.ndarray): The action to execute, must be 1-D with shape (action_dim,).
+
+        Returns:
+            observation (Dict[str, Any]): The formatted observation after the step.
+            reward (float): The scalar reward for this step.
+            terminated (bool): Whether the episode terminated successfully.
+            truncated (bool): Whether the episode was truncated due to a time limit.
+            info (Dict[str, Any]): Additional environment info.
+        """
+        if action.ndim != 1:
+            raise ValueError(
+                f"Expected action to be 1-D (shape (action_dim,)), "
+                f"but got shape {action.shape} with ndim={action.ndim}"
+            )
+        raw_obs, reward, done, truncated, info = self._env.step(action)
+
+        # Determine whether the task was successful
+        is_success = bool(info.get("success", 0))
+        terminated = done or is_success
+        info.update(
+            {
+                "task": self.task,
+                "done": done,
+                "is_success": is_success,
+            }
+        )
+
+        # Format the raw observation into the expected structure
+        observation = self._format_raw_obs(raw_obs)
+        if terminated:
+            info["final_info"] = {
+                "task": self.task,
+                "done": bool(done),
+                "is_success": bool(is_success),
+            }
+            self.reset()
+
+        return observation, reward, terminated, truncated, info
+
+    def close(self):
+        self._env.close()
+
+
+# ---- Main API ----------------------------------------------------------------
+
+
+def create_metaworld_envs(
+    task: str,
+    n_envs: int,
+    gym_kwargs: dict[str, Any] | None = None,
+    env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
+) -> dict[str, dict[int, Any]]:
+    """
+    Create vectorized Meta-World environments with a consistent return shape.
+
+    Returns:
+        dict[task_group][task_id] -> vec_env (env_cls([...]) with exactly n_envs factories)
+    Notes:
+        - n_envs is the number of rollouts *per task* (episode_index = 0..n_envs-1).
+        - `task` can be a single difficulty group (e.g., "easy", "medium", "hard") or a comma-separated list.
+        - If a task name is not in DIFFICULTY_TO_TASKS, we treat it as a single custom task.
+    """
+    if env_cls is None or not callable(env_cls):
+        raise ValueError("env_cls must be a callable that wraps a list of environment factory callables.")
+    if not isinstance(n_envs, int) or n_envs <= 0:
+        raise ValueError(f"n_envs must be a positive int; got {n_envs}.")
+
+    gym_kwargs = dict(gym_kwargs or {})
+    task_groups = [t.strip() for t in task.split(",") if t.strip()]
+    if not task_groups:
+        raise ValueError("`task` must contain at least one Meta-World task or difficulty group.")
+
+    print(f"Creating Meta-World envs | task_groups={task_groups} | n_envs(per task)={n_envs}")
+
+    out: dict[str, dict[int, Any]] = defaultdict(dict)
+
+    for group in task_groups:
+        # if not in difficulty presets, treat it as a single custom task
+        tasks = DIFFICULTY_TO_TASKS.get(group, [group])
+
+        for tid, task_name in enumerate(tasks):
+            print(f"Building vec env | group={group} | task_id={tid} | task={task_name}")
+
+            # build n_envs factories
+            fns = [(lambda tn=task_name: MetaworldEnv(task=tn, **gym_kwargs)) for _ in range(n_envs)]
+
+            out[group][tid] = env_cls(fns)
+
+    # return a plain dict for consistency
+    return {group: dict(task_map) for group, task_map in out.items()}

src/lerobot/envs/metaworld_config.json

@@ -0,0 +1,121 @@
+{
+  "TASK_DESCRIPTIONS": {
+    "assembly-v3": "Pick up a nut and place it onto a peg",
+    "basketball-v3": "Dunk the basketball into the basket",
+    "bin-picking-v3": "Grasp the puck from one bin and place it into another bin",
+    "box-close-v3": "Grasp the cover and close the box with it",
+    "button-press-topdown-v3": "Press a button from the top",
+    "button-press-topdown-wall-v3": "Bypass a wall and press a button from the top",
+    "button-press-v3": "Press a button",
+    "button-press-wall-v3": "Bypass a wall and press a button",
+    "coffee-button-v3": "Push a button on the coffee machine",
+    "coffee-pull-v3": "Pull a mug from a coffee machine",
+    "coffee-push-v3": "Push a mug under a coffee machine",
+    "dial-turn-v3": "Rotate a dial 180 degrees",
+    "disassemble-v3": "Pick a nut out of a peg",
+    "door-close-v3": "Close a door with a revolving joint",
+    "door-lock-v3": "Lock the door by rotating the lock clockwise",
+    "door-open-v3": "Open a door with a revolving joint",
+    "door-unlock-v3": "Unlock the door by rotating the lock counter-clockwise",
+    "hand-insert-v3": "Insert the gripper into a hole",
+    "drawer-close-v3": "Push and close a drawer",
+    "drawer-open-v3": "Open a drawer",
+    "faucet-open-v3": "Rotate the faucet counter-clockwise",
+    "faucet-close-v3": "Rotate the faucet clockwise",
+    "hammer-v3": "Hammer a screw on the wall",
+    "handle-press-side-v3": "Press a handle down sideways",
+    "handle-press-v3": "Press a handle down",
+    "handle-pull-side-v3": "Pull a handle up sideways",
+    "handle-pull-v3": "Pull a handle up",
+    "lever-pull-v3": "Pull a lever down 90 degrees",
+    "peg-insert-side-v3": "Insert a peg sideways",
+    "pick-place-wall-v3": "Pick a puck, bypass a wall and place the puck",
+    "pick-out-of-hole-v3": "Pick up a puck from a hole",
+    "reach-v3": "Reach a goal position",
+    "push-back-v3": "Push the puck to a goal",
+    "push-v3": "Push the puck to a goal",
+    "pick-place-v3": "Pick and place a puck to a goal",
+    "plate-slide-v3": "Slide a plate into a cabinet",
+    "plate-slide-side-v3": "Slide a plate into a cabinet sideways",
+    "plate-slide-back-v3": "Get a plate from the cabinet",
+    "plate-slide-back-side-v3": "Get a plate from the cabinet sideways",
+    "peg-unplug-side-v3": "Unplug a peg sideways",
+    "soccer-v3": "Kick a soccer into the goal",
+    "stick-push-v3": "Grasp a stick and push a box using the stick",
+    "stick-pull-v3": "Grasp a stick and pull a box with the stick",
+    "push-wall-v3": "Bypass a wall and push a puck to a goal",
+    "reach-wall-v3": "Bypass a wall and reach a goal",
+    "shelf-place-v3": "Pick and place a puck onto a shelf",
+    "sweep-into-v3": "Sweep a puck into a hole",
+    "sweep-v3": "Sweep a puck off the table",
+    "window-open-v3": "Push and open a window",
+    "window-close-v3": "Push and close a window"
+  },
+  "TASK_NAME_TO_ID": {
+    "assembly-v3": 0, "basketball-v3": 1, "bin-picking-v3": 2, "box-close-v3": 3,
+    "button-press-topdown-v3": 4, "button-press-topdown-wall-v3": 5, "button-press-v3": 6,
+    "button-press-wall-v3": 7, "coffee-button-v3": 8, "coffee-pull-v3": 9, "coffee-push-v3": 10,
+    "dial-turn-v3": 11, "disassemble-v3": 12, "door-close-v3": 13, "door-lock-v3": 14,
+    "door-open-v3": 15, "door-unlock-v3": 16, "drawer-close-v3": 17, "drawer-open-v3": 18,
+    "faucet-close-v3": 19, "faucet-open-v3": 20, "hammer-v3": 21, "hand-insert-v3": 22,
+    "handle-press-side-v3": 23, "handle-press-v3": 24, "handle-pull-side-v3": 25,
+    "handle-pull-v3": 26, "lever-pull-v3": 27, "peg-insert-side-v3": 28, "peg-unplug-side-v3": 29,
+    "pick-out-of-hole-v3": 30, "pick-place-v3": 31, "pick-place-wall-v3": 32,
+    "plate-slide-back-side-v3": 33, "plate-slide-back-v3": 34, "plate-slide-side-v3": 35,
+    "plate-slide-v3": 36, "push-back-v3": 37, "push-v3": 38, "push-wall-v3": 39, "reach-v3": 40,
+    "reach-wall-v3": 41, "shelf-place-v3": 42, "soccer-v3": 43, "stick-pull-v3": 44,
+    "stick-push-v3": 45, "sweep-into-v3": 46, "sweep-v3": 47, "window-open-v3": 48,
+    "window-close-v3": 49
+  },
+  "DIFFICULTY_TO_TASKS": {
+    "easy": [
+      "button-press-v3", "button-press-topdown-v3", "button-press-topdown-wall-v3",
+      "button-press-wall-v3", "coffee-button-v3", "dial-turn-v3", "door-close-v3",
+      "door-lock-v3", "door-open-v3", "door-unlock-v3", "drawer-close-v3", "drawer-open-v3",
+      "faucet-close-v3", "faucet-open-v3", "handle-press-v3", "handle-press-side-v3",
+      "handle-pull-v3", "handle-pull-side-v3", "lever-pull-v3", "plate-slide-v3",
+      "plate-slide-back-v3", "plate-slide-back-side-v3", "plate-slide-side-v3", "reach-v3",
+      "reach-wall-v3", "window-close-v3", "window-open-v3", "peg-unplug-side-v3"
+    ],
+    "medium": [
+      "basketball-v3", "bin-picking-v3", "box-close-v3", "coffee-pull-v3", "coffee-push-v3",
+      "hammer-v3", "peg-insert-side-v3", "push-wall-v3", "soccer-v3", "sweep-v3", "sweep-into-v3"
+    ],
+    "hard": [
+      "assembly-v3", "hand-insert-v3", "pick-out-of-hole-v3", "pick-place-v3", "push-v3", "push-back-v3"
+    ],
+    "very_hard": [
+      "shelf-place-v3", "disassemble-v3", "stick-pull-v3", "stick-push-v3", "pick-place-wall-v3"
+    ]
+  },
+  "TASK_POLICY_MAPPING": {
+    "assembly-v3": "SawyerAssemblyV3Policy", "basketball-v3": "SawyerBasketballV3Policy",
+    "bin-picking-v3": "SawyerBinPickingV3Policy", "box-close-v3": "SawyerBoxCloseV3Policy",
+    "button-press-topdown-v3": "SawyerButtonPressTopdownV3Policy",
+    "button-press-topdown-wall-v3": "SawyerButtonPressTopdownWallV3Policy",
+    "button-press-v3": "SawyerButtonPressV3Policy", "button-press-wall-v3": "SawyerButtonPressWallV3Policy",
+    "coffee-button-v3": "SawyerCoffeeButtonV3Policy", "coffee-pull-v3": "SawyerCoffeePullV3Policy",
+    "coffee-push-v3": "SawyerCoffeePushV3Policy", "dial-turn-v3": "SawyerDialTurnV3Policy",
+    "disassemble-v3": "SawyerDisassembleV3Policy", "door-close-v3": "SawyerDoorCloseV3Policy",
+    "door-lock-v3": "SawyerDoorLockV3Policy", "door-open-v3": "SawyerDoorOpenV3Policy",
+    "door-unlock-v3": "SawyerDoorUnlockV3Policy", "drawer-close-v3": "SawyerDrawerCloseV3Policy",
+    "drawer-open-v3": "SawyerDrawerOpenV3Policy", "faucet-close-v3": "SawyerFaucetCloseV3Policy",
+    "faucet-open-v3": "SawyerFaucetOpenV3Policy", "hammer-v3": "SawyerHammerV3Policy",
+    "hand-insert-v3": "SawyerHandInsertV3Policy", "handle-press-side-v3": "SawyerHandlePressSideV3Policy",
+    "handle-press-v3": "SawyerHandlePressV3Policy", "handle-pull-side-v3": "SawyerHandlePullSideV3Policy",
+    "handle-pull-v3": "SawyerHandlePullV3Policy", "lever-pull-v3": "SawyerLeverPullV3Policy",
+    "peg-insert-side-v3": "SawyerPegInsertionSideV3Policy", "peg-unplug-side-v3": "SawyerPegUnplugSideV3Policy",
+    "pick-out-of-hole-v3": "SawyerPickOutOfHoleV3Policy", "pick-place-v3": "SawyerPickPlaceV3Policy",
+    "pick-place-wall-v3": "SawyerPickPlaceWallV3Policy",
+    "plate-slide-back-side-v3": "SawyerPlateSlideBackSideV3Policy",
+    "plate-slide-back-v3": "SawyerPlateSlideBackV3Policy",
+    "plate-slide-side-v3": "SawyerPlateSlideSideV3Policy", "plate-slide-v3": "SawyerPlateSlideV3Policy",
+    "push-back-v3": "SawyerPushBackV3Policy", "push-v3": "SawyerPushV3Policy",
+    "push-wall-v3": "SawyerPushWallV3Policy", "reach-v3": "SawyerReachV3Policy",
+    "reach-wall-v3": "SawyerReachWallV3Policy", "shelf-place-v3": "SawyerShelfPlaceV3Policy",
+    "soccer-v3": "SawyerSoccerV3Policy", "stick-pull-v3": "SawyerStickPullV3Policy",
+    "stick-push-v3": "SawyerStickPushV3Policy", "sweep-into-v3": "SawyerSweepIntoV3Policy",
+    "sweep-v3": "SawyerSweepV3Policy", "window-open-v3": "SawyerWindowOpenV3Policy",
+    "window-close-v3": "SawyerWindowCloseV3Policy"
+  }
+}

src/lerobot/scripts/lerobot_eval.py

@@ -180,9 +180,15 @@ def rollout(
             render_callback(env)
 
         # VectorEnv stores is_success in `info["final_info"][env_index]["is_success"]`. "final_info" isn't
-        # available of none of the envs finished.
+        # available if none of the envs finished.
         if "final_info" in info:
-            successes = [info["is_success"] if info is not None else False for info in info["final_info"]]
+            final_info = info["final_info"]
+            if not isinstance(final_info, dict):
+                raise RuntimeError(
+                    "Unsupported `final_info` format: expected dict (Gymnasium >= 1.0). "
+                    "You're likely using an older version of gymnasium (< 1.0). Please upgrade."
+                )
+            successes = final_info["is_success"].tolist()
         else:
             successes = [False] * env.num_envs
 

tests/policies/test_policies.py

@@ -95,7 +95,6 @@ def test_get_policy_and_config_classes(policy_name: str):
 @pytest.mark.parametrize(
     "ds_repo_id,env_name,env_kwargs,policy_name,policy_kwargs",
     [
-        ("lerobot/xarm_lift_medium", "xarm", {}, "tdmpc", {"use_mpc": True}),
         ("lerobot/pusht", "pusht", {}, "diffusion", {}),
         ("lerobot/pusht", "pusht", {}, "vqbet", {}),
         ("lerobot/pusht", "pusht", {}, "act", {}),
@@ -328,8 +327,6 @@ def test_multikey_construction(multikey: bool):
         # TODO(alexander-soare): `policy.use_mpc=false` was previously the default in the config yaml but it
         # was changed to true. For some reason, tests would pass locally, but not in CI. So here we override
         # to test with `policy.use_mpc=false`.
-        ("lerobot/xarm_lift_medium", "tdmpc", {"use_mpc": False}, "use_policy"),
-        # ("lerobot/xarm_lift_medium", "tdmpc", {"use_mpc": True}, "use_mpc"),
         # TODO(rcadene): the diffusion model was normalizing the image in mean=0.5 std=0.5 which is a hack supposed to
         # to normalize the image at all. In our current codebase we dont normalize at all. But there is still a minor difference
         # that fails the test. However, by testing to normalize the image with 0.5 0.5 in the current codebase, the test pass.