让机器人学会应对新物体，结合大模型与规划算法自动发现并学习新操作。
Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
- 用大模型识别缺失操作，结合符号规划生成新计划。
- 在连续机器人环境中实现90%以上的操作发现成功率，优于现有方法。
- 适合需要自主适应新环境的机器人系统研发人员参考。
在动态开放世界环境中，自主智能体常遭遇阻碍其达成目标的新颖情况。传统符号规划器因缺乏与新物体交互所需的操作算子而无法生成有效计划。本文提出一种神经符号架构，融合符号规划、强化学习与大语言模型（LLM），以学习处理新物体。具体而言，利用LLM的常识推理能力识别缺失操作算子，生成包含新算子的规划，并编写奖励函数以指导强化学习代理学习新算子的控制策略。实验表明，该方法在连续机器人领域中，相较于现有最先进方法，在操作发现与学习方面均有显著提升，操作发现成功率达90%以上。
原文摘要 · Abstract (English)
In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, traditional symbolic planners fail to generate plans when the robot's planning domain lacks the operators that enable it to interact appropriately with novel objects in the environment. We propose a neuro-symbolic architecture that integrates symbolic planning, reinforcement learning, and a large language model (LLM) to learn how to handle novel objects. In particular, we leverage the common sense reasoning capability of the LLM to identify missing operators, generate plans with the symbolic AI planner, and write reward functions to guide the reinforcement learning agent in learning control policies for newly identified operators. Our method outperforms the state-of-the-art methods in operator discovery as well as operator learning in continuous robotic domains.
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