SieloRobotics/dit_3tasks_resumed

Model Card for multi_task_dit

Multi-Task Diffusion Transformer (DiT) extends Diffusion Policy with a large Diffusion Transformer and text + vision conditioning for multi-task robot learning. It supports both diffusion and flow-matching objectives and reaches high dexterity with only ~450M parameters.

This policy has been trained and pushed to the Hub using LeRobot.

Learn how to train and run it in the LeRobot multi_task_dit guide, or browse the full documentation.

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Model Details

• License: apache-2.0
• Robot type: arlo
• Cameras: top, wrist

Inputs & Outputs

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

Inputs

Feature	Type	Shape
observation.state	STATE	(7,)
observation.images.top	VISUAL	(3, 480, 640)
observation.images.wrist	VISUAL	(3, 480, 640)

Outputs

Feature	Type	Shape
action	ACTION	(7,)

Training Dataset

• Repository: SieloRobotics/3tasks_combined2_short
• Episodes: 310
• Frames: 199843
• Frame rate: 30 FPS
• Task(s): "bottle", "Crush", "Pepsi"

Training Configuration

Setting	Value
Training steps	100000
Batch size	64
Optimizer	adam
Learning rate	2e-05
Seed	1000
LeRobot version	0.6.1

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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=arlo \
  --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=SieloRobotics/dit_3tasks_resumed \
  --task="bottle" \
  --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=${HF_USER}/<dataset> \
  --policy.type=multi_task_dit \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${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}
}