pour_pi05_base

PyTorch pi05 baseline policy fine-tuned on the local UR5 real-robot LeRobot-format pour dataset.

• dataset: ur5_place_and_pour_nuts_camera_shifts
• training config name: pi05_ur5_pour_pytorch_baseline
• experiment name: pi05_ur5_pour_pytorch_baseline_tandon_2gpu_b16
• source Slurm job: 9753605
• base camera view: observation.images.context_left_rgb
• wrist camera view: observation.images.wrist_right_rgb
• base model init: /scratch/yz11445/pi05_base
• currently included checkpoint steps: 20000, 25000, 30000

This run has completed through 30000 steps.

Included Files

• config.json: base pi05 model config copied from the initialization checkpoint
• model_architecture_config.json: baseline architecture settings used by this run
• training_config_summary.json: training/data/run summary for this release
• assets/ur5_place_and_pour_nuts_camera_shifts/norm_stats.json: normalization statistics
• checkpoints/<step>/: hard-linked checkpoint snapshots with model.safetensors, metadata.pt, and hard-linked assets

Inference

Serve one of the included checkpoints with:

uv run scripts/serve_policy.py policy:checkpoint \
  --policy.config=pi05_ur5_pour_pytorch_baseline \
  --policy.dir=/path/to/pour_pi05_base/checkpoints/30000

Notes

• The policy loader uses the code-defined training config pi05_ur5_pour_pytorch_baseline from src/openpi/training/config.py.
• This baseline variant has geometric augmentation off and keeps pose/ray/view/cross-view/aux-point modules disabled.
• Release checkpoints intentionally exclude optimizer.pt.
• Normalization stats are also present inside each checkpoint asset tree; a top-level hard-linked copy is included for convenience.