Blame for LetterFaceAlignment/random-1500-v0/data/metadata.json

{"total_episodes": 1500, "total_steps": 1234005, "data_format": "hdf5", "observation_space": "{\"type\": \"Box\", \"dtype\": \"float32\", \"shape\": [27], \"low\": [-Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity, -Infinity], \"high\": [Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity]}", "action_space": "{\"type\": \"Box\", \"dtype\": \"float32\", \"shape\": [14], \"low\": [-3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732, -3.1415927410125732], \"high\": [3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732, 3.1415927410125732]}", "env_spec": "{\"id\": \"telearms/LetterFaceAlignment\", \"entry_point\": \"gymnasium_env.telearms_env:TeleArmsEnv\", \"reward_threshold\": null, \"nondeterministic\": false, \"max_episode_steps\": null, \"order_enforce\": true, \"disable_env_checker\": true, \"kwargs\": {\"task\": \"Letter Face Alignment\", \"obs_mode\": \"privileged\"}, \"additional_wrappers\": [], \"vector_entry_point\": null}", "dataset_id": "telearms/LetterFaceAlignment/random-1500-v0", "code_permalink": "https://github.com/liuwilli10101/telearms-gym", "author": ["TeleArms"], "algorithm_name": "human teleoperation", "description": "TeleArms YAM bimanual cube task 'Letter Face Alignment', human teleoperation demos in simulation: 1,500 demos per task drawn at random, the episodes of the LeRobot dataset mliu48/telearms-yam-cube-v5-exp-random-1500 for this task. State-based (privileged) version: observations are the 27-dim obs_mode='privileged' vector of gymnasium_env/telearms_env.py (14-dim policy joint state, each cube's position, wxyz quaternion and 6-dim velocity, each goal patch's position and wxyz quaternion); actions are 14-dim absolute joint targets at 30 Hz. Each demo is cut at its first success; rewards are sparse (1.0 on that step); infos['reward_dense'] is the env's dense reward (1/N per task stage first reached, plus 1.0 on success); demos that never succeed under the env's check end truncated.", "minari_version": "0.5.3", "dataset_size": 254.9}