anuragbhandari-eng/diffusion_libero_object

Diffusion Policy — LIBERO single-task (book → caddy)

Diffusion Policy trained with LeRobot on one LIBERO task:

pick up the book and place it in the back compartment of the caddy

Trained from scratch on a laptop GPU (RTX 4050, 6 GB VRAM).

Evaluation

Evaluated in the LIBERO simulator (libero_10, task 5) — the same task the policy was trained on. All 10 rollouts use LIBERO's canonical initial states with randomised object poses.

Task	Suite	Trials	Successes	Success rate
pick up the book and place it in the back compartment of the caddy	libero_10 task 5	10	6	60%

Per-episode outcomes (1 = success): [1, 0, 1, 1, 0, 1, 1, 0, 1, 0]

Reproduce:

lerobot-eval \
  --policy.path=anuragbhandari-eng/diffusion_libero_object \
  --env.type=libero --env.task=libero_10 --env.task_ids="[5]" \
  --env.observation_height=256 --env.observation_width=256 \
  --eval.n_episodes=10 --eval.batch_size=1 --env.max_parallel_tasks=1 \
  --output_dir=eval_out

---

Model Details

• License: apache-2.0
• Robot type: panda (Franka)
• Cameras: agentview (image) + wrist (image2)

Inputs & Outputs

Inputs

Feature	Type	Shape
observation.images.image	VISUAL	(3, 256, 256)
observation.images.image2	VISUAL	(3, 256, 256)
observation.state	STATE	(8,)

Outputs

Feature	Type	Shape
action	ACTION	(7,)

Training Dataset

• Repository: HuggingFaceVLA/libero
• Task: pick up the book and place it in the back compartment of the caddy
• Episodes used: 19 (episodes 27,28,47,55,61,64,81,103,104,109,111,127,133,136,141,147,154,158,159)
• Frames: 3 609
• Frame rate: 10.0 FPS

Training Configuration

Setting	Value
Training steps	80 000
Batch size	8
Optimizer	adam
Learning rate	0.0001
Seed	1000
Hardware	RTX 4050 Laptop 6 GB VRAM
LeRobot version	0.5.2

---

How to Reproduce Training

pip install -e ".[libero]" --no-build-isolation
export MUJOCO_GL=egl
lerobot-train \
  --policy.type=diffusion \
  --dataset.repo_id=HuggingFaceVLA/libero \
  --dataset.episodes="[27,28,47,55,61,64,81,103,104,109,111,127,133,136,141,147,154,158,159]" \
  --batch_size=8 --steps=80000 \
  --policy.device=cuda \
  --policy.push_to_hub=true \
  --policy.repo_id=anuragbhandari-eng/diffusion_libero_object \
  --save_freq=5000

---

Citation

@misc{cadene2024lerobot,
    author = {Cadene, Remi and others},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}