NeSyChain: A Neurosymbolic Framework for Lifelong Learning in Embodied Agents

Embodied agents operate in changing open-world environments and therefore require lifelong learning. Recent work addresses this with neurosymbolic agents, whose combination of foundation models and symbolic tools makes learned knowledge explicit and verifiable. Yet in these agents, both reasoning and learning are tightly coupled to a particular configuration of foundation models, symbolic tools, and knowledge format, and must be rebuilt whenever it changes. We present NeSyChain, a modular framework for building neurosymbolic embodied agents whose reasoning and learning are decoupled from the configuration. Foundation models and symbolic tools are loosely coupled through a shared knowledge layer that mediates their interactions, so reasoning flows stay intact when components change. Specifically, for lifelong learning, NeSyChain employs two-stage knowledge transformation: acquired data is consolidated into symbolic knowledge through contrastive validation, then compiled into neural memory on the frozen model, reducing tool reliance. NeSyChain remains effective across 6 neurosymbolic configurations, spanning 4 foundation models, 9 symbolic tools, and 3 knowledge formats, on 3 benchmarks and in real-world settings, using a common modular structure and learning procedure. On every benchmark, it outperforms the strongest baseline, with 21.8% higher task success and 51.3% lower symbolic tool reliance on average. In real-world deployment, verifying learned knowledge before use raises task success from 33.3% to 96.7%.