用贝叶斯物理推理让机器人更智能地适应未知环境。
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning
- 结合可微物理与贝叶斯推断，实现带不确定性的决策
- 通过元学习快速适应新任务，提升数据效率
- 适合研究自主系统与具身智能的学者
真实世界的机器人应用，从自主探索到辅助技术，需要具备适应性、可解释性和数据高效性的学习范式。尽管深度学习架构和基础模型在多种机器人应用中取得了显著进展，但在未知和动态环境中仍难以高效可靠运行。本文批判性评估了这些局限，并提出一种融合数据驱动学习与有意识、结构化推理的概念框架。具体而言，我们主张利用可微物理实现高效的环境建模，采用贝叶斯推断进行不确定性感知决策，以及借助元学习实现对新任务的快速适应。通过将物理符号推理嵌入神经模型，机器人可超越训练数据范围进行泛化，对新情境进行推理，并持续扩展知识。我们认为，此类混合神经符号架构是下一代自主系统的关键，为此，我们提供了一条研究路线图以指导并加速其发展。
原文摘要 · Abstract (English)
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.
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