Model Card for dot This is a policy trained with . / /resolve/main/demo.gif" width="60%"/> --> This policy has been trained and pushed to the Hub using . See the . --- ## Model Details - apache-2.0 - - ## Inputs & Outputs The policy consumes these observation features and produces these action features. | Feature | Type | Shape | | --- | --- | --- | | | STATE | | | | VISUAL | | | Feature | Type | Shape | | --- | --- | --- | | | ACTION | | ## Training Dataset - - 50 - 49165 - 57 FPS - "ball picking" ## Training Configuration | Setting | Value | | --- | --- | | Training steps | 20000 | | Batch size | 64 | | Optimizer | adamw | | Learning rate | 0.0001 | | Seed | 1000 | | LeRobot version | 0.6.0 | --- ## How to Get Started with the Model New to LeRobot? These guides cover the full workflow: - — set up the package. - — assemble, wire, and calibrate your robot and cameras. - — the end-to-end imitation-learning walkthrough. - — quick reference for the commands. The short version to run and train this policy: ### Run the policy on your robot Replace the remaining placeholders with your own values: 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 is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at . ### Train your own policy --- ## Evaluation --- ## Citation If you use this policy, please cite the method linked in the description above, along with LeRobot:
Writes checkpoints to outputs/train/ /checkpoints/.
No evaluation results have been provided for this policy yet.
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} }
This policy has been trained and pushed to the Hub using LeRobot.
