Bayesian Inverse Physics for Neuro-Symbolic Robot Learning

TL;DR

This paper advocates for hybrid neuro-symbolic robot learning architectures that combine differentiable physics, Bayesian inference, and meta-learning to enhance adaptability, interpretability, and data efficiency in autonomous systems.

Contribution

It introduces a conceptual framework integrating structured reasoning with data-driven models, and provides a research roadmap for developing such hybrid neuro-symbolic architectures.

Findings

Proposes differentiable physics for efficient world modeling

Utilizes Bayesian inference for uncertainty-aware decision-making

Suggests meta-learning for rapid adaptation to new tasks

Abstract

Real-world robotic applications, from autonomous exploration to assistive technologies, require adaptive, interpretable, and data-efficient learning paradigms. While deep learning architectures and foundation models have driven significant advances in diverse robotic applications, they remain limited in their ability to operate efficiently and reliably in unknown and dynamic environments. In this position paper, we critically assess these limitations and introduce a conceptual framework for combining data-driven learning with deliberate, structured reasoning. Specifically, we propose leveraging differentiable physics for efficient world modeling, Bayesian inference for uncertainty-aware decision-making, and meta-learning for rapid adaptation to new tasks. By embedding physical symbolic reasoning within neural models, robots could generalize beyond their training data, reason about novel situations, and continuously expand their knowledge. We argue that such hybrid neuro-symbolic architectures are essential for the next generation of autonomous systems, and to this end, we provide a research roadmap to guide and accelerate their development.