AI RESEARCH
EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models
arXiv CS.AI
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ArXi:2605.11859v1 Announce Type: cross Robot navigation is a crucial task with applications to social robots in dynamic human environments. While Reinforcement Learning (RL) has shown great promise for this problem, the policy quality is highly sensitive to the specification of reward functions. Hand-crafted rewards require substantial domain expertise and embed inductive biases that are difficult to audit or adapt, limiting their effectiveness and leading to suboptimal performance.