# CHOP: Counterfactual human preference labels improve obstacle avoidance in visuomotor navigation policies

- Source: arXiv cs.RO
- Published: 2026-11-02T05:00:00.000Z
- RoboHot: https://robohot.news/en/items/jsd1em0rqtcwqe029ikdrfgsr
- Original: https://arxiv.org/abs/2603.02004

## Summary

The authors propose CHOP, which fine-tunes visuomotor navigation policies with counterfactual human preference labels to align them with human intuition about safety and obstacle avoidance. On the SCAND dataset, multiple advanced models fine-tuned with CHOP reduced near-collision events by an average of 49.7%, decreased deviation from human-preferred trajectories by 45.0%, and increased average obstacle clearance by 19.8%.