Automated harness improves robot task success in new scenes
A system adds tools that sit between a robot's fixed decision maker and its actions, improving results without retraining.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Shengguang Wu, Suning Huang, Cherie Ho, Ria Doshi, Hao Zhu, Serena Yeung-Levy
- Universities and research institutions
- Stanford University
Institution metadata: Original paper ↗
What they did and found
An add-on layer sits around a robot’s fixed decision policy. It adds tools and memory, learns by trial and error, and helps the robot do better in new scenes without changing the main policy.
Why it matters
This could help robots work in new settings without costly retraining, especially when tasks or places differ from what the robot saw during its original learning.
The add-on starts simple and tests small changes on new tasks, reducing the need for large adjustments to the main policy.
In practice, the robot could learn where to grab and how to approach objects, all without touching its core controller.
What remains uncertain
Evaluations were conducted in simulation with up to five harness-optimization attempts; real-world transfer and longer-term gains still require testing.
Original sources · 1
Check the original paper for its authors, methods, version and access terms.
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