robotics
The Robot's Reward Made Room for Personal Space
A Gaussian-mixture model of proxemics improved reported social-navigation metrics without sacrificing task efficiency.
Summary
A Gaussian-mixture model of proxemics improved reported social-navigation metrics without sacrificing task efficiency.
The reward gives a reinforcement-learning navigator a dense cost for entering modeled personal-space fields within its view. Across simulated crowds and densities, it improved social metrics while remaining competitive on navigation; real-world human studies remain future evidence.
Why it matters
A Gaussian-mixture model of proxemics improved reported social-navigation metrics without sacrificing task efficiency.
Limits and context
- Across simulated crowds and densities, it improved social metrics while remaining competitive on navigation; real-world human studies remain future evidence.
Key claims
A Gaussian-mixture model of proxemics improved reported social-navigation metrics without sacrificing task efficiency.
Qualification: Across simulated crowds and densities, it improved social metrics while remaining competitive on navigation; real-world human studies remain future evidence.
Evidence: source-2026-08-15-019
Sources
- arXiv preprint 2608.12917arXiv · primary research
Corrections
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