SkiP-RLBench

SkiP — Pretrained Checkpoints (RLBench-10)

Pretrained checkpoints for SkiP (Skip Policy) on 10 RLBench tasks, as presented in the paper SkiP: When to Skip and When to Refine for Efficient Robot Manipulation.

SkiP dynamically leaps over redundant free-space steps and concentrates control on contact-rich key segments via action relabeling — no learned skip planner or hierarchical structure required.

Links

• Paper: https://arxiv.org/abs/2605.15536
• Code: https://github.com/CCCalcifer/Skip-Policy
• Project Page: https://pgq18.github.io/SkiP-page/

Checkpoints

Training config: kf_mode=skip, skip.window=16, skip.high_quantile=0.75, 20k steps, 100 demos per task.

Usage

# Install
git clone https://github.com/CCCalcifer/Skip-Policy.git && cd Skip-Policy
bash scripts/init.sh

# One-click eval (auto-downloads checkpoint + data)
bash scripts/eval.sh task=push_button

# Or manually download and eval
pip install huggingface_hub
python -c "from huggingface_hub import hf_hub_download; hf_hub_download('AYouZhuA/SkiP-RLBench', 'checkpoints/open_drawer.pt', local_dir='.')"
python scripts/eval.py task=open_drawer snapshot=checkpoints/open_drawer.pt

Citation

@misc{dai2026skipskiprefineefficient,
      title={SkiP: When to Skip and When to Refine for Efficient Robot Manipulation},
      author={Mingtong Dai and Guanqi Peng and Yongjie Bai and Feng Yan and Chunjie Chen and Lingbo Liu and Liang Lin and Xinyu Wu},
      year={2026},
      eprint={2605.15536},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2605.15536},
}