# Move, don't think: Sample-efficient collision avoidance learning with lossy proximity sensors

- Source: arXiv cs.RO
- Published: 2026-10-26T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/nhp5vil6eg535yeb8sgb3w0on
- Original: https://arxiv.org/abs/2610.31891

## Summary

This paper investigates whole-body proximity sensors as an inductive bias for learning reflexive collision-avoidance behavior, finding that low-resolution lossy sensors were a stronger bias than high-resolution full coverage. The study compared eight candidate representations in a reinforcement learning environment, including latent compression, future contact anticipation, lossy corruption, and privileged state estimation, and trained over 200 humanoid robots end-to-end to play a simplified version of dodgeball under intentionally constrained sample and compute budgets. The authors also found that anticipating when and where contact occurs actually reduced learning sample efficiency.