EvoNav: 大規模言語モデルを用いたロボットナビゲーションの進化的報酬関数設計
EvoNav: Evolutionary Reward Function Design for Robot Navigation with Large Language Models
ロボットナビゲーションの強化学習における報酬関数の設計を、大規模言語モデルと進化的アルゴリズムで自動化する手法を提案した論文。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zhikai Zhao, Chuanbo Hua, Federico Berto, Zihan Ma, Kanghoon Lee, Jiachen Li, Jinkyoo Park
分類: cs.RO, cs.AI
原文アブストラクト
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 from the LLM via a progressive three-stage warm-up-boost procedure. EvoNav advances from analytical proxies with low-cost surrogates, such as small datasets and analytic rules, to lightweight rollouts and, finally, to full policy training, enabling computationally efficient exploration under effective feedback. Experiment results show that EvoNav produces more effective navigation policies than manually designed RL rewards and state-of-the-art reward design methods.
