EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models
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. In this paper, we propose EvoNav, an evolutionary framework that automates the design of robot navigation reward functions via large language models (LLMs). To overcome prohibitively costly policy training, EvoNav evaluates each candidate proposal ...
| Authors | Zhikai Zhao, Chuanbo Hua, Federico Berto et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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