让机器人通过接触感知优化行为，高效学习物体参数。
Behavior Synthesis via Contact-Aware Fisher Information Maximization
- 基于接触感知的费舍尔信息最大化，设计信息丰富的交互行为。
- 生成能高效学习物体参数的机器人动作，适用于多种参数学习场景。
- 在真实机器人实验中验证了接触导向行为的有效性，适合具身学习研究者。
接触动力学蕴含丰富信息，可提升机器人通过交互理解环境的能力。然而，由于接触数据固有的稀疏性和非光滑性，获取高信息量的接触数据极具挑战，需采用主动策略以最大化接触的利用价值。本文提出一种最优实验设计方法，用于合成产生高信息量接触数据的机器人行为。该方法构建了接触感知的费舍尔信息度量，表征有助于参数学习的接触行为。实验观察到机器人涌现出能有效激发接触交互的行为，可高效学习物体参数。最后，在多个机器人实验中验证了接触感知对参数学习的显著提升效果。
原文摘要 · Abstract (English)
Contact dynamics hold immense amounts of information that can improve a robot's ability to characterize and learn about objects in their environment through interactions. However, collecting information-rich contact data is challenging due to its inherent sparsity and non-smooth nature, requiring an active approach to maximize the utility of contacts for learning. In this work, we investigate an optimal experimental design approach to synthesize robot behaviors that produce contact-rich data for learning. Our approach derives a contact-aware Fisher information measure that characterizes information-rich contact behaviors that improve parameter learning. We observe emergent robot behaviors that are able to excite contact interactions that efficiently learns object parameters across a range of parameter learning examples. Last, we demonstrate the utility of contact-awareness for learning parameters through contact-seeking behaviors on several robotic experiments.
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