add libero

examples/5_train_libero.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+
+# config
+REPO_ID=physical-intelligence/libero
+TASK=libero_10
+OUTPUT_DIR=./outputs/train_run/smolvla2_libero
+
+# clean previous run
+rm -rf $OUTPUT_DIR
+
+# training params
+STEPS=100000
+BATCH_SIZE=4
+EVAL_FREQ=2000
+SAVE_FREQ=10000
+NUM_WORKERS=0
+
+# model params
+POLICY=smolvla
+USE_AMP=false
+OPTIMIZER_LR=1e-4
+PEFT_METHOD=lora
+LOAD_VLM_WEIGHTS=true
+VLM_REPO_ID=None
+MAX_ACTION_DIM=32
+MAX_STATE_DIM=32
+
+# dataset/image params
+USE_IMAGENET_STATS=false
+ENABLE_IMG_TRANSFORM=true
+MAX_NUM_IMAGES=2
+MAX_IMAGE_DIM=1024
+
+echo -e "\033[1;33m[WARNING]\033[0m LIBERO is not yet fully supported in this PR!"
+# launch
+MUJOCO_GL=egl python src/lerobot/scripts/train.py \
+  --policy.type=$POLICY \
+  --dataset.repo_id=$REPO_ID \
+  --env.type=libero \
+  --env.task=$TASK \
+  --output_dir=$OUTPUT_DIR \
+  --steps=$STEPS \
+  --batch_size=$BATCH_SIZE \
+  --eval_freq=$EVAL_FREQ \
+  --save_freq=$SAVE_FREQ \
+  --num_workers=$NUM_WORKERS \
+  --policy.max_action_dim=$MAX_ACTION_DIM \
+  --policy.max_state_dim=$MAX_STATE_DIM \
+  --policy.use_amp=$USE_AMP \
+  --policy.optimizer_lr=$OPTIMIZER_LR \
+  --policy.peft_method=$PEFT_METHOD \
+  --policy.load_vlm_weights=$LOAD_VLM_WEIGHTS \
+  --policy.repo_id=$VLM_REPO_ID \
+  --dataset.use_imagenet_stats=$USE_IMAGENET_STATS \
+  --dataset.image_transforms.enable=$ENABLE_IMG_TRANSFORM \
+  --dataset.max_num_images=$MAX_NUM_IMAGES \
+  --dataset.max_image_dim=$MAX_IMAGE_DIM \
+  # --policy.exclude_image_keys=wrist_image \
+  # --policy.use_env_state=false

src/lerobot/constants.py

@@ -20,6 +20,7 @@ from huggingface_hub.constants import HF_HOME
 OBS_ENV_STATE = "observation.environment_state"
 OBS_STATE = "observation.state"
 OBS_IMAGE = "observation.image"
+OBS_IMAGE_2 = "observation.image2"
 OBS_IMAGES = "observation.images"
 ACTION = "action"
 REWARD = "next.reward"

src/lerobot/envs/configs.py

@@ -14,12 +14,12 @@
 
 import abc
 from dataclasses import dataclass, field
-from typing import Any
+from typing import Any, Optional
 
 import draccus
 
 from lerobot.configs.types import FeatureType, PolicyFeature
-from lerobot.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGES, OBS_STATE
+from lerobot.constants import ACTION, OBS_ENV_STATE, OBS_IMAGE, OBS_IMAGE_2, OBS_IMAGES, OBS_STATE
 from lerobot.robots import RobotConfig
 from lerobot.teleoperators.config import TeleoperatorConfig
 
@@ -30,6 +30,8 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
     fps: int = 30
     features: dict[str, PolicyFeature] = field(default_factory=dict)
     features_map: dict[str, str] = field(default_factory=dict)
+    multitask_eval: bool = False
+    max_parallel_tasks: int = 5
 
     @property
     def type(self) -> str:
@@ -44,7 +46,7 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
 @EnvConfig.register_subclass("aloha")
 @dataclass
 class AlohaEnv(EnvConfig):
-    task: str | None = "AlohaInsertion-v0"
+    task: str = "AlohaInsertion-v0"
     fps: int = 50
     episode_length: int = 400
     obs_type: str = "pixels_agent_pos"
@@ -82,7 +84,7 @@ class AlohaEnv(EnvConfig):
 @EnvConfig.register_subclass("pusht")
 @dataclass
 class PushtEnv(EnvConfig):
-    task: str | None = "PushT-v0"
+    task: str = "PushT-v0"
     fps: int = 10
     episode_length: int = 300
     obs_type: str = "pixels_agent_pos"
@@ -124,7 +126,7 @@ class PushtEnv(EnvConfig):
 @EnvConfig.register_subclass("xarm")
 @dataclass
 class XarmEnv(EnvConfig):
-    task: str | None = "XarmLift-v0"
+    task: str = "XarmLift-v0"
     fps: int = 15
     episode_length: int = 200
     obs_type: str = "pixels_agent_pos"
@@ -179,10 +181,10 @@ class EnvTransformConfig:
     add_joint_velocity_to_observation: bool = False
     add_current_to_observation: bool = False
     add_ee_pose_to_observation: bool = False
-    crop_params_dict: dict[str, tuple[int, int, int, int]] | None = None
-    resize_size: tuple[int, int] | None = None
+    crop_params_dict: Optional[dict[str, tuple[int, int, int, int]]] = None
+    resize_size: Optional[tuple[int, int]] = None
     control_time_s: float = 20.0
-    fixed_reset_joint_positions: Any | None = None
+    fixed_reset_joint_positions: Optional[Any] = None
     reset_time_s: float = 5.0
     use_gripper: bool = True
     gripper_quantization_threshold: float | None = 0.8
@@ -195,25 +197,24 @@ class EnvTransformConfig:
 class HILSerlRobotEnvConfig(EnvConfig):
     """Configuration for the HILSerlRobotEnv environment."""
 
-    robot: RobotConfig | None = None
-    teleop: TeleoperatorConfig | None = None
-    wrapper: EnvTransformConfig | None = None
+    robot: Optional[RobotConfig] = None
+    teleop: Optional[TeleoperatorConfig] = None
+    wrapper: Optional[EnvTransformConfig] = None
     fps: int = 10
     name: str = "real_robot"
-    mode: str | None = None  # Either "record", "replay", None
-    repo_id: str | None = None
-    dataset_root: str | None = None
-    task: str | None = ""
+    mode: str = None  # Either "record", "replay", None
+    repo_id: Optional[str] = None
+    dataset_root: Optional[str] = None
+    task: str = ""
     num_episodes: int = 10  # only for record mode
     episode: int = 0
     device: str = "cuda"
     push_to_hub: bool = True
-    pretrained_policy_name_or_path: str | None = None
-    reward_classifier_pretrained_path: str | None = None
+    pretrained_policy_name_or_path: Optional[str] = None
+    reward_classifier_pretrained_path: Optional[str] = None
     # For the reward classifier, to record more positive examples after a success
     number_of_steps_after_success: int = 0
 
-    @property
     def gym_kwargs(self) -> dict:
         return {}
 
@@ -223,8 +224,9 @@ class HILSerlRobotEnvConfig(EnvConfig):
 class HILEnvConfig(EnvConfig):
     """Configuration for the HIL environment."""
 
+    type: str = "hil"
     name: str = "PandaPickCube"
-    task: str | None = "PandaPickCubeKeyboard-v0"
+    task: str = "PandaPickCubeKeyboard-v0"
     use_viewer: bool = True
     gripper_penalty: float = 0.0
     use_gamepad: bool = True
@@ -248,18 +250,18 @@ class HILEnvConfig(EnvConfig):
         }
     )
     ################# args from hilserlrobotenv
-    reward_classifier_pretrained_path: str | None = None
-    robot_config: RobotConfig | None = None
-    teleop_config: TeleoperatorConfig | None = None
-    wrapper: EnvTransformConfig | None = None
-    mode: str | None = None  # Either "record", "replay", None
-    repo_id: str | None = None
-    dataset_root: str | None = None
+    reward_classifier_pretrained_path: Optional[str] = None
+    robot_config: Optional[RobotConfig] = None
+    teleop_config: Optional[TeleoperatorConfig] = None
+    wrapper: Optional[EnvTransformConfig] = None
+    mode: str = None  # Either "record", "replay", None
+    repo_id: Optional[str] = None
+    dataset_root: Optional[str] = None
     num_episodes: int = 10  # only for record mode
     episode: int = 0
     device: str = "cuda"
     push_to_hub: bool = True
-    pretrained_policy_name_or_path: str | None = None
+    pretrained_policy_name_or_path: Optional[str] = None
     # For the reward classifier, to record more positive examples after a success
     number_of_steps_after_success: int = 0
     ############################
@@ -271,3 +273,55 @@ class HILEnvConfig(EnvConfig):
             "use_gamepad": self.use_gamepad,
             "gripper_penalty": self.gripper_penalty,
         }
+
+
+@EnvConfig.register_subclass("libero")
+@dataclass
+class LiberoEnv(EnvConfig):
+    task: str = "libero_10"  # can also choose libero_spatial, libero_object, etc.
+    fps: int = 30
+    episode_length: int = 520
+    obs_type: str = "pixels_agent_pos"
+    render_mode: str = "rgb_array"
+    camera_name: str = "agentview_image,robot0_eye_in_hand_image"
+    init_states: bool = True
+    multitask_eval: bool = True
+    features: dict[str, PolicyFeature] = field(
+        default_factory=lambda: {
+            "action": PolicyFeature(type=FeatureType.ACTION, shape=(7,)),
+        }
+    )
+    features_map: dict[str, str] = field(
+        default_factory=lambda: {
+            "action": ACTION,
+            "agent_pos": OBS_STATE,
+            "pixels/agentview_image": f"{OBS_IMAGE}",
+            "pixels/robot0_eye_in_hand_image": f"{OBS_IMAGE_2}",
+        }
+    )
+
+    def __post_init__(self):
+        if self.obs_type == "pixels":
+            self.features["pixels/agentview_image"] = PolicyFeature(
+                type=FeatureType.VISUAL, shape=(360, 360, 3)
+            )
+            self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature(
+                type=FeatureType.VISUAL, shape=(360, 360, 3)
+            )
+        elif self.obs_type == "pixels_agent_pos":
+            self.features["agent_pos"] = PolicyFeature(type=FeatureType.STATE, shape=(8,))
+            self.features["pixels/agentview_image"] = PolicyFeature(
+                type=FeatureType.VISUAL, shape=(360, 360, 3)
+            )
+            self.features["pixels/robot0_eye_in_hand_image"] = PolicyFeature(
+                type=FeatureType.VISUAL, shape=(360, 360, 3)
+            )
+
+    @property
+    def gym_kwargs(self) -> dict:
+        return {
+            # "task": self.task,
+            "obs_type": self.obs_type,
+            "render_mode": self.render_mode,
+            # "max_episode_steps": self.episode_length,
+        }

src/lerobot/envs/libero.py

@@ -0,0 +1,311 @@
+import math
+import os
+from collections import defaultdict
+from itertools import chain
+from typing import Any, Callable
+
+import gymnasium as gym
+import numpy as np
+import torch
+from gymnasium import spaces
+from libero.libero import benchmark, get_libero_path
+from libero.libero.envs import OffScreenRenderEnv
+
+from lerobot.constants import (
+    OBS_IMAGE,
+    OBS_IMAGE_2,
+)
+
+
+def create_libero_envs(
+    task: str,
+    n_envs: int,
+    gym_kwargs: dict[str, Any] = None,
+    camera_name: str = "agentview_image,robot0_eye_in_hand_image",
+    init_states: bool = True,
+    env_cls: Callable = None,
+    multitask_eval: bool = True,
+) -> dict[str, dict[str, Any]]:
+    """
+    Here n_envs is per task and equal to the number of rollouts.
+    Returns:
+        dict[str, dict[str, list[LiberoEnv]]]: keys are task_suite and values are list of LiberoEnv envs.
+    """
+    if gym_kwargs is None:
+        gym_kwargs = {}
+
+    if not multitask_eval:
+        benchmark_dict = benchmark.get_benchmark_dict()
+        task_suite = benchmark_dict[task]()  # can also choose libero_spatial, libero_object, libero_10 etc.
+        tasks_id = list(range(len(task_suite.tasks)))
+        episode_indices = [0 for i in range(len(tasks_id))]
+        if len(tasks_id) == 1:
+            tasks_id = [tasks_id[0] for _ in range(n_envs)]
+            episode_indices = list(range(n_envs))
+        elif len(tasks_id) < n_envs and n_envs % len(tasks_id) == 0:
+            n_repeat = n_envs // len(tasks_id)
+            episode_indices = []
+            for i in range(len(tasks_id)):
+                episode_indices.extend(list(range(n_repeat)))
+            tasks_id = list(chain.from_iterable([[item] * n_repeat for item in tasks_id]))
+        elif n_envs < len(tasks_id):
+            tasks_id = tasks_id[:n_envs]
+            episode_indices = list(range(n_envs))[:n_envs]
+            print(f"WARNING: n_envs < len(tasks_id), evaluating only on {tasks_id}")
+        print(f"Creating Libero envs with task ids {tasks_id} from suite {task}")
+        assert n_envs == len(tasks_id), (
+            f"len(n_envs) and tasks_id should be the same, got {n_envs} and {len(tasks_id)}"
+        )
+        return env_cls(
+            [
+                lambda i=i: LiberoEnv(
+                    task_suite=task_suite,
+                    task_id=tasks_id[i],
+                    task_suite_name=task,
+                    camera_name=camera_name,
+                    init_states=init_states,
+                    episode_index=episode_indices[i],
+                    **gym_kwargs,
+                )
+                for i in range(n_envs)
+            ]
+        )
+    else:
+        envs = defaultdict(dict)
+        benchmark_dict = benchmark.get_benchmark_dict()
+        task = task.split(",")
+        for _task in task:
+            task_suite = benchmark_dict[
+                _task
+            ]()  # can also choose libero_spatial, libero_object, libero_10 etc.
+            tasks_ids = list(range(len(task_suite.tasks)))
+            # tasks_ids = [0] # FIXME(mshukor): debug
+            for tasks_id in tasks_ids:
+                episode_indices = list(range(n_envs))
+                print(
+                    f"Creating Libero envs with task ids {tasks_id} from suite {_task}, episode_indices: {episode_indices}"
+                )
+                envs_list = [
+                    lambda i=i: LiberoEnv(
+                        task_suite=task_suite,
+                        task_id=tasks_id,
+                        task_suite_name=_task,
+                        camera_name=camera_name,
+                        init_states=init_states,
+                        episode_index=episode_indices[i],
+                        **gym_kwargs,
+                    )
+                    for i in range(n_envs)
+                ]
+                envs[_task][tasks_id] = env_cls(envs_list)
+        return envs
+
+
+def quat2axisangle(quat):
+    """
+    Copied from robosuite: https://github.com/ARISE-Initiative/robosuite/blob/eafb81f54ffc104f905ee48a16bb15f059176ad3/robosuite/utils/transform_utils.py#L490C1-L512C55
+
+    Converts quaternion to axis-angle format.
+    Returns a unit vector direction scaled by its angle in radians.
+
+    Args:
+        quat (np.array): (x,y,z,w) vec4 float angles
+
+    Returns:
+        np.array: (ax,ay,az) axis-angle exponential coordinates
+    """
+    # clip quaternion
+    if quat[3] > 1.0:
+        quat[3] = 1.0
+    elif quat[3] < -1.0:
+        quat[3] = -1.0
+
+    den = np.sqrt(1.0 - quat[3] * quat[3])
+    if math.isclose(den, 0.0):
+        # This is (close to) a zero degree rotation, immediately return
+        return np.zeros(3)
+
+    return (quat[:3] * 2.0 * math.acos(quat[3])) / den
+
+
+def get_task_init_states(task_suite, i):
+    init_states_path = os.path.join(
+        get_libero_path("init_states"),
+        task_suite.tasks[i].problem_folder,
+        task_suite.tasks[i].init_states_file,
+    )
+    init_states = torch.load(init_states_path, weights_only=False)
+    return init_states
+
+
+def get_libero_dummy_action():
+    """Get dummy/no-op action, used to roll out the simulation while the robot does nothing."""
+    return [0, 0, 0, 0, 0, 0, -1]
+
+
+class LiberoEnv(gym.Env):
+    metadata = {"render_modes": ["rgb_array"], "render_fps": 80}
+
+    def __init__(
+        self,
+        task_suite,
+        task_id,
+        task_suite_name,
+        camera_name="agentview_image,robot0_eye_in_hand_image",
+        obs_type="pixels",
+        render_mode="rgb_array",
+        observation_width=256,
+        observation_height=256,
+        visualization_width=640,
+        visualization_height=480,
+        init_states=True,
+        episode_index=0,
+    ):
+        super().__init__()
+        self.task_id = task_id
+        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.init_states = init_states
+        self.camera_name = camera_name.split(
+            ","
+        )  # agentview_image (main) or robot0_eye_in_hand_image (wrist)
+        self.camera_name_mapping = {
+            "agentview_image": OBS_IMAGE,
+            "robot0_eye_in_hand_image": OBS_IMAGE_2,
+        }
+        self.num_steps_wait = (
+            10  # Do nothing for the first few timesteps to wait for the simulator drops objects
+        )
+        self.episode_index = episode_index
+
+        self._env = self._make_envs_task(task_suite, self.task_id)
+        if task_suite_name == "libero_spatial":
+            max_steps = 220  # longest training demo has 193 steps
+        elif task_suite_name == "libero_object":
+            max_steps = 280  # longest training demo has 254 steps
+        elif task_suite_name == "libero_goal":
+            max_steps = 300  # longest training demo has 270 steps
+        elif task_suite_name == "libero_10":
+            max_steps = 520  # longest training demo has 505 steps
+        elif task_suite_name == "libero_90":
+            max_steps = 400  # longest training demo has 373 steps
+        self._max_episode_steps = max_steps
+
+        images = {}
+        for cam in self.camera_name:
+            images[self.camera_name_mapping[cam]] = spaces.Box(
+                low=0,
+                high=255,
+                shape=(self.observation_height, self.observation_width, 3),
+                dtype=np.uint8,
+            )
+
+        if self.obs_type == "state":
+            raise NotImplementedError()
+        elif self.obs_type == "pixels":
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Dict(images),
+                }
+            )
+        elif self.obs_type == "pixels_agent_pos":
+            self.observation_space = spaces.Dict(
+                {
+                    "pixels": spaces.Dict(images),
+                    "agent_pos": spaces.Box(
+                        low=-1000.0,
+                        high=1000.0,
+                        shape=(8,),
+                        dtype=np.float64,
+                    ),
+                }
+            )
+
+        self.action_space = spaces.Box(low=-1, high=1, shape=(7,), dtype=np.float32)
+
+    def render(self):
+        raw_obs = self._env.env._get_observations()
+        image = self._format_raw_obs(raw_obs)["pixels"][OBS_IMAGE]
+        return image
+
+    def _make_envs_task(self, task_suite, task_id: int = 0):
+        task = task_suite.get_task(task_id)
+        self.task = task.name
+        self.task_description = task.language
+        task_bddl_file = os.path.join(get_libero_path("bddl_files"), task.problem_folder, task.bddl_file)
+
+        env_args = {
+            "bddl_file_name": task_bddl_file,
+            "camera_heights": self.observation_height,
+            "camera_widths": self.observation_width,
+        }
+        env = OffScreenRenderEnv(**env_args)
+        env.reset()
+        if self.init_states:
+            init_states = get_task_init_states(
+                task_suite, task_id
+            )  # for benchmarking purpose, we fix the a set of initial states FIXME(mshukor): should be in the reset()?
+            init_state_id = self.episode_index  # episode index
+            env.set_init_state(init_states[init_state_id])
+
+        return env
+
+    def _format_raw_obs(self, raw_obs):
+        images = {}
+        for camera_name in self.camera_name:
+            image = raw_obs[camera_name]
+            image = image[::-1, ::-1]  # rotate 180 degrees
+            images[self.camera_name_mapping[camera_name]] = image
+        # images = image if len(images) == 1 else images
+        state = np.concatenate(
+            (
+                raw_obs["robot0_eef_pos"],
+                quat2axisangle(raw_obs["robot0_eef_quat"]),
+                raw_obs["robot0_gripper_qpos"],
+            )
+        )
+        agent_pos = state
+        if self.obs_type == "state":
+            raise NotImplementedError()
+        elif self.obs_type == "pixels":
+            obs = {"pixels": images.copy()}
+        elif self.obs_type == "pixels_agent_pos":
+            obs = {
+                "pixels": images.copy(),
+                "agent_pos": agent_pos,
+            }
+        return obs
+
+    def reset(self, seed=None, **kwargs):
+        super().reset(seed=seed)
+
+        self._env.seed(seed)
+        raw_obs = self._env.reset()
+        # Do nothing for the first few timesteps to wait for the simulator drops objects
+        for _ in range(self.num_steps_wait):
+            raw_obs, _, _, _ = self._env.step(get_libero_dummy_action())
+        observation = self._format_raw_obs(raw_obs)
+        info = {"is_success": False}
+        return observation, info
+
+    def step(self, action):
+        assert action.ndim == 1
+        raw_obs, reward, done, info = self._env.step(action)
+
+        is_success = self._env.check_success()
+        terminated = done or is_success
+        info["is_success"] = done  # is_success
+
+        observation = self._format_raw_obs(raw_obs)
+        if done:
+            self.reset()
+            print(self.task, self.task_id, done, is_success)
+        truncated = False
+        return observation, reward, terminated, truncated, info
+
+    def close(self):
+        self._env.close()

src/lerobot/scripts/eval.py

@@ -50,12 +50,12 @@ import json
 import logging
 import threading
 import time
-from collections.abc import Callable
 from contextlib import nullcontext
 from copy import deepcopy
 from dataclasses import asdict
 from pathlib import Path
 from pprint import pformat
+from typing import Callable
 
 import einops
 import gymnasium as gym
@@ -456,55 +456,179 @@ def _compile_episode_data(
 
     return data_dict
 
-
 @parser.wrap()
-def eval_main(cfg: EvalPipelineConfig):
+def eval(cfg: EvalPipelineConfig):
     logging.info(pformat(asdict(cfg)))
 
     # Check device is available
-    device = get_safe_torch_device(cfg.policy.device, log=True)
+    device = get_safe_torch_device(cfg.device, log=True)
 
     torch.backends.cudnn.benchmark = True
     torch.backends.cuda.matmul.allow_tf32 = True
-    set_seed(cfg.seed)
+    set_global_seed(cfg.seed)
 
-    logging.info(colored("Output dir:", "yellow", attrs=["bold"]) + f" {cfg.output_dir}")
+    log_output_dir(cfg.output_dir)
 
     logging.info("Making environment.")
     env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
 
     logging.info("Making policy.")
-
     policy = make_policy(
         cfg=cfg.policy,
+        device=device,
         env_cfg=cfg.env,
     )
     policy.eval()
 
-    with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext():
-        info = eval_policy(
-            env,
-            policy,
-            cfg.eval.n_episodes,
-            max_episodes_rendered=10,
-            videos_dir=Path(cfg.output_dir) / "videos",
-            start_seed=cfg.seed,
-        )
-    print(info["aggregated"])
+    with torch.no_grad(), torch.autocast(device_type=device.type) if cfg.use_amp else nullcontext():
+        if cfg.env.multitask_eval:
+            info = eval_policy_multitask(
+                env,
+                policy,
+                cfg.eval.n_episodes,
+                max_episodes_rendered=10,
+                videos_dir=Path(cfg.output_dir) / "videos",
+                start_seed=cfg.seed,
+                max_parallel_tasks=cfg.env.max_parallel_tasks,
+                verbose=False,
+            )
+            # Print overall stats
+            print("Overall Aggregated Metrics:")
+            print(info["overall"]["aggregated"])
+
+            # Print per-suite stats
+            for task_group, task_group_info in info.items():
+                if task_group == "overall":
+                    continue  # Skip the overall stats since we already printed it
+                print(f"\nAggregated Metrics for {task_group}:")
+                print(task_group_info["aggregated"])
+            for _task_group, v in env.items():
+                for _env in v.values():
+                    _env.close()
+        else:
+            info = eval_policy(
+                env,
+                policy,
+                cfg.eval.n_episodes,
+                max_episodes_rendered=10,
+                videos_dir=Path(cfg.output_dir) / "videos",
+                start_seed=cfg.seed,
+            )
+            print(info["aggregated"])
+            env.close()
 
     # Save info
     with open(Path(cfg.output_dir) / "eval_info.json", "w") as f:
         json.dump(info, f, indent=2)
 
-    env.close()
-
     logging.info("End of eval")
 
+def eval_policy_multitask(
+    envs: dict[str, dict[str, gym.vector.VectorEnv]],
+    policy,
+    n_episodes: int,
+    max_episodes_rendered: int = 0,
+    videos_dir: Path | None = None,
+    return_episode_data: bool = False,
+    start_seed: int | None = None,
+    max_parallel_tasks: int = 5,
+    verbose: bool = True,
+) -> dict:
+    global_start = time.time()
+    results = {}
 
-def main():
-    init_logging()
-    eval_main()
+    overall_rewards, overall_max_rewards, overall_successes = [], [], []
+    overall_video_paths = []
+    overall_episode_data = None
 
+    def eval_task(task_group, task_id, env):
+        """Evaluates a single task in parallel."""
+        print(f"Evaluating: task_group: {task_group}, task_id: {task_id} ...")
+        task_result = eval_policy(
+            env, policy, n_episodes, max_episodes_rendered, videos_dir, return_episode_data, start_seed, verbose=verbose
+        )
+
+        per_episode = task_result["per_episode"]
+        return {
+            "task_group": task_group,
+            "task_id": task_id,
+            "sum_rewards": [ep["sum_reward"] for ep in per_episode],
+            "max_rewards": [ep["max_reward"] for ep in per_episode],
+            "successes": [ep["success"] for ep in per_episode],
+            "video_paths": task_result.get("video_paths", []),
+        }
+
+    with concurrent.futures.ThreadPoolExecutor(max_workers=max_parallel_tasks) as executor:
+        future_to_task = {
+            executor.submit(eval_task, task_group, task_id, env): (task_group, task_id)
+            for task_group, tasks in envs.items()
+            for task_id, env in tasks.items()
+        }
+
+        task_group_results = {}
+
+        for future in concurrent.futures.as_completed(future_to_task):
+            task_result = future.result()
+            task_group = task_result["task_group"]
+
+            if task_group not in task_group_results:
+                task_group_results[task_group] = {
+                    "sum_rewards": [],
+                    "max_rewards": [],
+                    "successes": [],
+                    "video_paths": [],
+                }
+
+            task_group_results[task_group]["sum_rewards"].extend(task_result["sum_rewards"])
+            task_group_results[task_group]["max_rewards"].extend(task_result["max_rewards"])
+            task_group_results[task_group]["successes"].extend(task_result["successes"])
+            task_group_results[task_group]["video_paths"].extend(task_result["video_paths"])
+
+    # Process results per task group
+    for task_group, data in task_group_results.items():
+        suite_rewards = data["sum_rewards"]
+        suite_max_rewards = data["max_rewards"]
+        suite_successes = data["successes"]
+        suite_video_paths = data["video_paths"]
+
+        suite_eval_s = time.time() - global_start
+        suite_eval_ep_s = suite_eval_s / max(1, len(suite_rewards))
+
+        results[task_group] = {
+            "aggregated": {
+                "avg_sum_reward": float(np.nanmean(suite_rewards)),
+                "avg_max_reward": float(np.nanmean(suite_max_rewards)),
+                "pc_success": float(np.nanmean(suite_successes) * 100),
+                "eval_s": suite_eval_s,
+                "eval_ep_s": suite_eval_ep_s,
+            },
+            "video_paths": suite_video_paths,
+            "episodes": None,  # Modify if episode data is needed
+        }
+
+        overall_rewards.extend(suite_rewards)
+        overall_max_rewards.extend(suite_max_rewards)
+        overall_successes.extend(suite_successes)
+        overall_video_paths.extend(suite_video_paths)
+
+    # Global metrics
+    global_eval_s = time.time() - global_start
+    global_eval_ep_s = global_eval_s / max(1, len(overall_rewards))
+
+    results["overall"] = {
+        "aggregated": {
+            "avg_sum_reward": float(np.nanmean(overall_rewards)),
+            "avg_max_reward": float(np.nanmean(overall_max_rewards)),
+            "pc_success": float(np.nanmean(overall_successes) * 100),
+            "eval_s": global_eval_s,
+            "eval_ep_s": global_eval_ep_s,
+        },
+        "video_paths": overall_video_paths,
+        "episodes": overall_episode_data,
+    }
+
+    return results
 
 if __name__ == "__main__":
-    main()
+    init_logging()
+    eval_main()

src/lerobot/scripts/train.py

@@ -34,7 +34,7 @@ from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies.factory import make_policy
 from lerobot.policies.pretrained import PreTrainedPolicy
 from lerobot.policies.utils import get_device_from_parameters
-from lerobot.scripts.eval import eval_policy
+from lerobot.scripts.eval import eval_policy, eval_policy_multitask
 from lerobot.utils.logging_utils import AverageMeter, MetricsTracker
 from lerobot.utils.random_utils import set_seed
 from lerobot.utils.train_utils import (
@@ -252,14 +252,32 @@ def train(cfg: TrainPipelineConfig):
                 torch.no_grad(),
                 torch.autocast(device_type=device.type) if cfg.policy.use_amp else nullcontext(),
             ):
-                eval_info = eval_policy(
-                    eval_env,
-                    policy,
-                    cfg.eval.n_episodes,
-                    videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
-                    max_episodes_rendered=4,
-                    start_seed=cfg.seed,
-                )
+                if cfg.env.multitask_eval:
+                            eval_info = eval_policy_multitask(
+                                eval_env,
+                                policy,
+                                cfg.eval.n_episodes,
+                                videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
+                                max_episodes_rendered=4,
+                                start_seed=cfg.seed,
+                                max_parallel_tasks=cfg.env.max_parallel_tasks,
+                            )
+                            aggregated_results = eval_info["overall"]["aggregated"]
+                            # Print per-suite stats
+                            for task_group, task_group_info in eval_info.items():
+                                if task_group == "overall":
+                                    continue  # Skip the overall stats since we already printed it
+                                print(f"\nAggregated Metrics for {task_group}:")
+                                print(task_group_info["aggregated"])
+                else:
+                    eval_info = eval_policy(
+                        eval_env,
+                        policy,
+                        cfg.eval.n_episodes,
+                        videos_dir=cfg.output_dir / "eval" / f"videos_step_{step_id}",
+                        max_episodes_rendered=4,
+                        start_seed=cfg.seed,
+                    )
 
             eval_metrics = {
                 "avg_sum_reward": AverageMeter("∑rwrd", ":.3f"),