Breaking Task Impasses Quickly: Adaptive Neuro-Symbolic Learning for Open-World Robotics

TL;DR

This paper introduces an adaptive neuro-symbolic framework that combines hierarchical planning and reinforcement learning to enable robots to adapt quickly to new and unforeseen environments, improving efficiency and robustness.

Contribution

The proposed framework uniquely integrates symbolic goal-oriented learning with world model exploration for rapid adaptation in open-world robotics.

Findings

Faster convergence compared to existing methods

Enhanced sample efficiency in robotic tasks

Greater robustness in dynamic environments

Abstract

Adapting to unforeseen novelties in open-world environments remains a major challenge for autonomous systems. While hybrid planning and reinforcement learning (RL) approaches show promise, they often suffer from sample inefficiency, slow adaptation, and catastrophic forgetting. We present a neuro-symbolic framework integrating hierarchical abstractions, task and motion planning (TAMP), and reinforcement learning to enable rapid adaptation in robotics. Our architecture combines symbolic goal-oriented learning and world model-based exploration to facilitate rapid adaptation to environmental changes. Validated in robotic manipulation and autonomous driving, our approach achieves faster convergence, improved sample efficiency, and superior robustness over state-of-the-art hybrid methods, demonstrating its potential for real-world deployment.