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Saurabh-sinha-209/
pick_place_masked_v2-dit

Name: pick_place_masked_v2-dit
Availability: InStock

Saurabh-sinha-209/pick_place_masked_v2

2507.05331

apache-2.0

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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.

Model Details

License: apache-2.0
Robot type: franka
Cameras: camera1, camera2

Inputs & Outputs

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

Inputs

Feature	Type	Shape
`observation.images.camera1`	VISUAL	`(3, 256, 256)`
`observation.images.camera2`	VISUAL	`(3, 256, 256)`
`observation.state`	STATE	`(18,)`

Outputs

Feature	Type	Shape
`action`	ACTION	`(7,)`

Training Dataset

Repository: Saurabh-sinha-209/pick_place_masked_v2
Episodes: 349
Frames: 34574
Frame rate: 15 FPS
Task(s): "put the blue cylinder into the orange bin", "put the green capsule into the orange bin", "put the green capsule into the purple bin", "put the red box into the purple bin", "put the red box into the orange bin", "put the blue cylinder into the purple bin"

Training Configuration

Setting	Value
Training steps	60000
Batch size	16
Optimizer	adam
Learning rate	2e-05
Seed	1000
LeRobot version	0.6.0

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

bash

lerobot-rollout \
  --strategy.type=base \
  --robot.type=franka \
  --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=Saurabh-sinha-209/pick_place_masked_v2-dit \
  --task="put the blue cylinder into the orange 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

bash

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/.

Evaluation

No evaluation results have been provided for this policy yet.

Citation

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

bibtex

@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}
}