aiqbench analysis
This research presents a neuro-symbolic architecture that combines symbolic planning, reinforcement learning, and large language models to enable autonomous agents to adapt to novel objects in dynamic environments. By leveraging an LLM's common sense reasoning to identify missing operators and generate reward functions, the system effectively bridges the gap between traditional planning limitations and real-world robotic variability. The approach demonstrates superior performance in operator discovery and learning within continuous robotic domains, marking a significant step forward in robust AI agent design.
Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
arXiv:2603.11351v2 Announce Type: replace Abstract: 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.Our webpage and code can be access here: helenlu66.github.io/hybridLLMguided/
Abstract: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 this http URL webpage and code can be access here: this http URL
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2603.11351 [cs.RO]
(or arXiv:2603.11351v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.11351 Focus to learn more arXiv-issued DOI via DataCite
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