身体性ナビゲーションのためのエージェント的自己進化的再計画
Agentic Self-Evolutionary Replanning for Embodied Navigation
複雑環境での身体性ナビゲーションの失敗に対処するため、ロボット自身を実行時学習で進化させる自己進化的再計画法(SERP)を提案。適応的機能調整とグラフ上のLLM推論により、成功率向上とトークン消費削減を実現。
詳しい要約
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著者: Guoliang Li, Ruihua Han, Chengyang Li, He Li, Shuai Wang, Wenchao Ding, Hong Zhang, Chengzhong Xu
分類: cs.RO
原文アブストラクト
Failure is inevitable for embodied navigation in complex environments. To enhance the resilience, replanning (RP) is a viable option, where the robot is allowed to fail, but is capable of adjusting plan until success. However, existing RP approaches freeze the ego action model and miss the opportunities to explore better plans by upgrading the robot itself. To address this limitation, we propose Self-Evolutionary RePlanning, or SERP for short, which leads to a paradigm shift from frozen models towards evolving models by run-time learning from recent experiences. In contrast to existing model evolution approaches that often get stuck at predefined static parameters, we introduce agentic self-evolving action model that uses in-context learning with auto-differentiation (ILAD) for adaptive function adjustment and global parameter reset. To achieve token-efficient replanning for SERP, we also propose graph chain-of-thought (GCOT) replanning with large language model (LLM) inference over distilled graphs. Extensive simulation and real-world experiments demonstrate that SERP achieves higher success rate with lower token expenditure over various benchmarks, validating its superior robustness and efficiency across diverse environments.
