NeSyKC: Neurosymbolic Knowledge Compilation For Lifelong Learning Embodied Agents

Lifelong learning requires embodied agents to continuously understand the environment and learn how to use that understanding to guide action, enabling future interactions to yield more informative experiences. Prior language model (LM)-based agents have primarily addressed these requirements in isolation, emphasizing either declarative knowledge acquisition or procedural knowledge reuse, leaving the continual interplay between the two during lifelong learning underexplored. We introduce Neurosymbolic Knowledge Compilation (NeSyKC), a lifelong learning framework that couples declarative knowledge formation with incremental compilation into procedural knowledge through a shared symbolic representation. In NeSyKC, accumulated experience is abstracted into symbolic rules that capture reusable environmental structure, and these rules provide the basis for deriving executable procedures that are incrementally compiled into neural adapters. This creates a feedback loop in which experience is abstracted into symbolic rules and compiled into procedures that guide the LM's reasoning on future tasks, yielding further experience for learning. Across four open-ended environments instantiated from established embodied benchmarks, NeSyKC expands and refines reusable knowledge across tasks, improving task performance and inference efficiency while supporting generalization to unseen tasks. Real-world experiments on two robots further show that the agent continues to learn from post-deployment experience, allowing acquired knowledge to be compiled for reuse in future tasks.