lichtphyz/diffusion_v3_desktop_skip_hue_sharpness

Diffusion Policy ablation: SO-101 block to bin, without hue and sharpness augmentation

An ablation of lichtphyz/diffusion_v3_desktop: the same Diffusion Policy, data and settings, but without the two slowest image augmentations (hue and sharpness). Those two took most of the data-loading time, so this run trained in 5 h 13 min instead of 11 h 20 min, with the same final training loss (0.003). It keeps brightness, contrast and saturation jitter and a small random affine transform.

Part of the SO-101 block-to-bin collection (models, training data and real-robot evaluations). Code, training commands and lessons learned: github.com/Lichtphyz/so101-block-to-bin.

Not evaluated on the real robot. For a tested model, use lichtphyz/diffusion_v3_desktop (8/10). Run this one the same way (DDIM, 5 denoising steps).

Trained with LeRobot; the training command is in docs/training.md.

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Model Details

• License: apache-2.0
• Robot type: so_follower
• Cameras: front, wrist

Inputs & Outputs

The policy consumes these observation features and produces these action features.

Inputs

Feature	Type	Shape
observation.state	STATE	(6,)
observation.images.front	VISUAL	(3, 480, 640)
observation.images.wrist	VISUAL	(3, 480, 640)

Outputs

Feature	Type	Shape
action	ACTION	(6,)

Training Dataset

• Repository: lichtphyz/so101_block_to_bin_v3
• Episodes: 125
• Frames: 55677
• Frame rate: 30 FPS
• Task(s): "Put the block in the bin."

Training Configuration

Setting	Value
Training steps	60000
Batch size	32
Optimizer	adam
Learning rate	0.0001
Seed	1000
LeRobot version	0.6.2

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How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

• Install LeRobot — set up the lerobot package.
• Hardware setup — assemble, wire, and calibrate your robot and cameras.
• Record data & train a policy — the end-to-end imitation-learning walkthrough.
• CLI cheat-sheet — quick reference for the lerobot-* commands.

The short version to run and train this policy:

Run the policy on your robot

lerobot-rollout \
  --strategy.type=base \
  --robot.type=so_follower \
  --robot.port=<your_robot_port> \
  --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
  --policy.path=lichtphyz/diffusion_v3_desktop_skip_hue_sharpness \
  --task="Put the block in the bin." \
  --duration=60

Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.

When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at rollout documentation.

Train your own policy

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=diffusion \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.

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Evaluation

No evaluation results have been provided for this policy yet.

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Citation

If you use this policy, please cite the method linked in the description above, along with LeRobot:

@misc{cadene2024lerobot,
    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}