AmberHyunKIM/act_libero_goal_task0

ACT on LIBERO goal task 0 (open the middle drawer of the cabinet)

Single-task ACT policy trained with LeRobot on one LIBERO task.

Task

open the middle drawer of the cabinet in LIBERO libero_goal, task index 0 (task_index=19 in lerobot/libero).

Training

Dataset	lerobot/libero, 43 episodes of this task only
Steps	100,000
Batch size	8
Params	51.6M
Hardware	1× RTX 5070 Ti (16 GB)
Wall clock	57 min

Evaluation

lerobot-eval, libero_goal task 0, seed 1000, 10 episodes, n_action_steps=10:

Checkpoint	Success rate	VRAM peak	Sec/episode
20k steps	60.0 %	1283 MiB	3.6
100k steps (this)	100.0 %	1283 MiB	2.7

Reference points measured under the same protocol on the same task:

Policy	Params	Success rate	VRAM peak
ACT (this)	0.05 B	100.0 %	1283 MiB
SmolVLA	0.5 B	100.0 %	1975 MiB
MolmoAct2	5 B	90.0 %	13541 MiB

Scope and limitations

ACT is not language-conditioned, so this checkpoint only covers the task it was trained on. Evaluated zero-shot on a neighbouring task (libero_goal task 1) it scores 0.0 % (0/10). Use it for this drawer task only; train a separate policy per task.

Usage

lerobot-eval \
  --policy.path=AmberHyunKIM/act_libero_goal_task0 \
  --policy.device=cuda \
  --policy.n_action_steps=10 \
  --env.type=libero --env.task=libero_goal --env.task_ids='[0]' \
  --eval.batch_size=1 --eval.n_episodes=10 --seed=1000

Requires MUJOCO_GL=egl on a headless machine.