README.md

metadata

license: apache-2.0
task_categories:
  - reinforcement-learning
  - robotics
tags:
  - Minari
  - TeleArms
  - bimanual
  - simulation
  - offline-rl

TeleArms offline RL datasets (state)

30 Minari datasets of human teleoperated demonstrations of 10 bimanual YAM cube tasks in MuJoCo: each task in three subsets of 1,500 demos, picked at random (random-1500), by player ELO (elo-1500) and by demo score (score-1500). The ids are telearms/<Task>/<subset>-v0:

import os
import minari
from huggingface_hub import snapshot_download

root = os.environ.get("MINARI_DATASETS_PATH", os.path.expanduser("~/.minari/datasets"))
snapshot_download("mliu48/telearms", repo_type="dataset", local_dir=os.path.join(root, "telearms"),
                  allow_patterns="CubePatchPlacement/random-1500-v0/*")
dataset = minari.load_dataset("telearms/CubePatchPlacement/random-1500-v0")
env = dataset.recover_environment()  # needs telearms-gym installed

The environment is TeleArmsEnv(task, obs_mode="privileged") in telearms-gym (gymnasium_env/telearms_env.py). On a machine without EGL, set MUJOCO_GL=disable before importing it.

Observations are the simulator's state, not images: the robot's 14 joint and gripper values, then each cube's position, orientation and velocity (13 numbers per cube), then each goal patch's position and orientation (7 per patch).

Task	Observation size
CubePatchPlacement	54
FrontEdgeCubeRow	53
FrontToBackCubeLine	53
LCubeArrangement	66
LetterFaceAlignment	27
RedCubeInFrontOfBlueCube	40
RedLeftOfBluePlacement	40
SemanticCubeOnRedPatchAUp	34
SequentialPushBlockToTarget	54
ThreeCubeWallRow	53

Rewards. Every demo was replayed in its task's environment and scored by its rules. rewards is the sparse reward, 1.0 on the step that reaches success, else 0. infos["reward_dense"] is the environment's reward_type="dense" reward: 1/N on the first step that reaches each of the task's N stages (grasp, lift, place, ...) plus 1.0 on success, at most 2 per episode. Each demo ends at its first success (terminations), as the environment ends an episode. Of the 45,000 demos, 665 (1.5%) were accepted when played but never satisfy the environment's task check; they end with truncations and no reward.

The same demos with camera images are the offline-rl branch of mliu48/telearms-yam-cube-v5-exp-<subset>: LeRobotDataset("mliu48/telearms-yam-cube-v5-exp-random-1500", revision="offline-rl").