Skill-Space Shooting Improves Robot Policies Autonomously, With Cross-Task Sharing Limits
Foundation-model guided corrections from reusable skills steadily boost task policies, but gains vary by task and require initial setup.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Zihang Rui, Renhao Wang, Haoxu Huang, Yang Gao
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
A preprint shows autonomous skill-based corrections improve four real-world robot tasks over iterations without additional human demonstrations; a shared skill library helps adaptation to new tasks and reduces extra teaching, though results differ by task.
Why it matters
Using reusable, short-horizon skills lets teams fix failing policies with less ongoing human input; example: reuse from Stack-3 to sponge-wiping tasks.
Skill-space shooting uses a fixed library of short-horizon skills and foundation-model guidance to test targeted corrections at policy failures, then trains the policy on successful repairs. It can improve unaided performance across tasks and supports sharing that reduces new teaching.
A meaningful limitation is that detectors and skill selection are trained per task; generalization across completely new tasks may require additional setup and data.
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What remains uncertain
Limitations include per-task failure detectors and dependency on initial demonstrations; cross-task gains may vary with task structure.
Original sources · 1
- Skill-Space Shooting for Autonomous Robot Policy Improvement ↗arXiv · 2026-09-29
Check the original paper for its authors, methods, version and access terms.
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