Bayesian Inverse Physics for Neuro-Symbolic Robot Learning

2025-06-10

AI Summary

Robots exhibit poor generalization, weak interpretability, and low data efficiency when adaptively learning in unknown dynamic environments. Method: This paper proposes a neuro-symbolic learning framework integrating physics-informed priors with data-driven learning. It unifies differentiable physics modeling, Bayesian uncertainty inference, and meta-learning within a single architecture. Specifically, dynamics constraints are encoded via a differentiable physics engine; cognitive uncertainty is quantified through Bayesian inverse problem solving; and neural-symbolic joint optimization, combined with meta-learning–driven few-shot task adaptation, enhances decision interpretability, cross-scenario transferability, and sample efficiency. Contribution/Results: Experiments demonstrate strong generalization under dynamic disturbances and out-of-distribution conditions, alongside robust continual learning capability. The framework establishes a reliable, transparent, and data-efficient learning paradigm for next-generation autonomous robots.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Neuro-Symbolic Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Efficient and reliable operation in dynamic environments
• Combining data-driven learning with structured reasoning
• Generalizing beyond training data for novel situations

Innovation

• Differentiable physics for efficient world modeling
• Bayesian inference for uncertainty-aware decisions
• Meta-learning for rapid task adaptation