pi05-real-cab-higher-firsthalf-60-droid-refined-lora

LoRA fine-tune of pi0.5 (pi05_droid warm-start) on IDEAS-Lab-Northwestern/real-cab-higher-firsthalf-60-droid-refined — 60 real Franka teleop trajectories of the first half of the cabinet task (open the cabinet, keep the door area clear), DROID schema.

• Task prompt: "Open the cabinet. Keep the area around the door clear."
• Warm-start: gs://openpi-assets/checkpoints/pi05_droid/params (clean, fresh)
• LoRA: gemma_2b_lora (rank 16) + gemma_300m_lora (rank 32), batch 4, EMA off.
• Norm-stats: reuses pi05_droid's bundled DROID assets (no recompute).

Checkpoint ladder

Dir	Cumulative steps	Train loss
10000/	10k	0.0046
20000/	20k	0.0032
30000/	30k	0.0029
40000/	40k	0.0038
50000/	50k (saved at step 49999)	0.0031

Train loss bottoms at ~30k; eval-sweep 20k–40k.

Refined real setup

Refined real setup (raised-Z cabinet) — refined wrist & main-camera poses and gripper. Wrist = raw cam1, exterior = raw cam0. Collector: yypeng666.

Schema (DROID) / inference

exterior_image_1_left ← exterior cam0, wrist_image_left ← wrist cam1, exterior_image_2_left zero-pad; joint_position(7)+gripper_position(1) state; actions(8)=joint_velocity(7)+next gripper target(1). Serve with openpi pi05_droid_finetune_lora. train_state/ omitted (inference-only). Eval on a real Franka, not the SFT server.

Paper & Citation

Part of ManiGuard: paper (arXiv:2608.17386) · code · docs

@misc{peng2026maniguard,
  title         = {{MANIGUARD}: A Benchmark and Data Suite for Specification-Grounded
                   Safety Evaluation and Improvement of Robotic Manipulation},
  author        = {Peng, Yiyan and Wang, Philip and Zhan, Simon Sinong and Lyu, Yiqi
                   and Ni, Zhenyang and Yan, Jixin and Wong, Fiorelli and Jiao, Ruochen
                   and Yin, Hang and Cao, Xinyu and Shao, Huajie and Li, Manling
                   and Zhang, Ruohan and Zhu, Qi},
  year          = {2026},
  eprint        = {2608.17386},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url           = {https://arxiv.org/abs/2608.17386},
}

License

The fine-tuned weights derive from a Physical Intelligence openpi base model whose VLM backbone is PaliGemma; use of these weights is therefore subject to the Gemma Terms of Use (including the Gemma Prohibited Use Policy), which downstream users must pass on. The openpi training code and ManiGuard's own contributions are Apache-2.0.