Bayesian Inverse Physics for Neuro-Symbolic Robot Learning
This is an incremental position paper proposing a research roadmap for neuro-symbolic architectures to improve adaptability and interpretability in robotics.
The paper tackles the limitations of deep learning in robotics for unknown and dynamic environments by proposing a conceptual framework that combines data-driven learning with structured reasoning, aiming to enable robots to generalize beyond training data and adapt efficiently.
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.