pi05_lego_mega_plus

Model Card for pi05

lerobot-train --dataset.repo_id=staudi25/lego_pickup_mega_plus --job_name=pi05_so101_lego_pickup --policy.type=pi05 --policy.pretrained_path=staudi25/pi05_lego_pickup_mega --policy.device=cuda --wandb.enable=false --dataset.image_transforms.enable=true --log_freq=1 --steps=3000 --save_freq=3000 --policy.repo_id=staudi25/pi05_lego_mega_plus --policy.dtype=bfloat16 --output_dir=outputs/train/pi05_lego_mega_plus --policy.compile_model=true --policy.gradient_checkpointing=true --policy.freeze_vision_encoder=false --policy.train_expert_only=false --batch_size=32

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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.

This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.

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How to Get Started with the Model

For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:

Train from scratch

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true

Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.

Evaluate the policy/run inference

lerobot-record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
  --episodes=10

Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.

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Model Details

• License: apache-2.0