Pi05 5k Precision Multicolour Block Pick Place Stage2

--- datasets: justintiensmith/red_block_precision-multicolour_block_pick_place library_name: lerobot license: apache-2.0 model_name: pi05 pipeline_tag: robotics tags: - pi05 - robotics - lerobot --- # Model Card for pi05 (model D) **π0.5 (Pi05) Policy** π0.5 is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository. **Model Overview** π0.5 represents a significant evolution from π0, developed by Physical Intelligence to address a big challenge in robotics: open-world generalization. While robots can perform impressive tasks in controlled environments, π0.5 is designed to generalize to entirely new environments and situations that were never seen during training. For more details, see the [Physical Intelligence π0.5 blog post](https://www.physicalintelligence.company/blog/pi05). This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/ \ --policy.type=act \ --output_dir=outputs/train/ \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/ --wandb.enable=true ``` _Writes checkpoints to `outputs/train/ /checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id= /eval_ \ --policy.path= / \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0