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This paper introduces a neuro-symbolic architecture that combines symbolic planning, reinforcement learning, and a large language model (LLM) to enable robots to handle novel objects in dynamic environments. The LLM is used to identify missing operators in the symbolic planning domain, generate plans, and write reward functions for RL agents to learn control policies for these new operators. Experiments demonstrate that this approach outperforms state-of-the-art methods in both operator discovery and learning in continuous robotic domains.
Robots can now learn to manipulate novel objects in dynamic environments by using LLMs to bridge the gap between symbolic planning and reinforcement learning.
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.
