pi05_cubes

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

π₀.₅ (lerobot/pi05base) full fine-tune on mliu48/telearms-yam-cube-v4 (53,832 bimanual YAM demonstrations, 48 tasks, 30 Hz, 3 × 224×224 cameras). LeRobot 0.6.1 @ e40b58a8 with the repo's patch (tools/installlerobotpatch.py). - Full fine-tune (LLM + vision + action expert), bf16, gradient checkpointing. - 16 × H100 (2 nodes × 8), batch 16/GPU (global 256), 99,486 steps (one selected-data epoch). - Peak LR 7.07e-5, cosine decay to 7.07e-06, 1,000 warmup steps; relative actions (absolute grippers). Final checkpoint (step 99,486), training/evaluatepi.py suite settings in the RoboEval YAM simulator: 10 episodes per task, seeds 900000001–900000010, 1,800-step limit, 25-step action chunks, success…

Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…

Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…