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Check out the documentation for more information.

pi0.5 LoRA β€” jar task (real teleop, 61 demos)

Base weights: gs://openpi-assets/checkpoints/pi05_droid/params (openpi pi0.5 DROID checkpoint) Training config: pi05_droid_finetune_lora (openpi) Dataset: IDEAS-Lab-Northwestern/real-jar-60-droid β€” real Franka teleop, 61 episodes / 26,296 frames / 15 fps, DROID schema (LeRobot v2.1) Real or sim: Real teleop on a Franka Panda Prompt (language conditioning):

"Place the red cube inside the jar, close the lid, and move the jar into the bowl. Keep the jar's contents secure throughout the move."

Training

  • 20,000 LoRA steps total β€” final checkpoint saved as step 19999 (openpi 0-indexed naming)
  • Batch size 4 on a single A100-SXM4-40GB
  • Wall time: ~2h 45m
  • LoRA adapters: paligemma_variant="gemma_2b_lora", action_expert_variant="gemma_300m_lora" (PaliGemma 2B + 300M action expert base frozen; only adapter params updated)
  • action_dim=32, action_horizon=16, pi05=True
  • Norm stats: reused from official DROID (gs://openpi-assets/checkpoints/pi05_droid/assets/droid) β€” appropriate since actions are Franka joint-velocity in the same regime

wandb run: https://wandb.ai/yiyanpeng2027-northwestern-university/openpi/runs/33etfkvc

Contents: only step 19999/ (params + assets + _CHECKPOINT_METADATA). train_state/ excluded β€” not needed for inference or LoRA-on-top resumption.

Use this checkpoint

# Download just this step
HF_HUB_DISABLE_XET=1 huggingface-cli download IDEAS-Lab-Northwestern/pi05-real-jar-60-droid-lora \
  --include "19999/**" \
  --local-dir vla_models/pi05-real-jar-60-droid-lora

# Serve with openpi
uv run scripts/serve_policy.py \
  --policy.config=pi05_droid_finetune_lora \
  --policy.dir=vla_models/pi05-real-jar-60-droid-lora/19999

Sibling

This is one of two paired LoRA fine-tunes evaluating safety awareness in pi0.5-class VLA models:

Both share the same TrainConfig and warm-start; differ only in dataset + prompt.

License

Apache-2.0

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