Behavior Synthesis via Contact-Aware Fisher Information Maximization

TL;DR

This paper introduces a contact-aware Fisher information measure to synthesize robot behaviors that generate informative contact data, enhancing object parameter learning through optimized interactions.

Contribution

It proposes a novel contact-aware Fisher information measure for designing robot behaviors that maximize contact data utility for learning.

Findings

Emergent robot behaviors effectively excite contact interactions for parameter learning.

Contact-aware behaviors improve learning efficiency across various object parameters.

Experimental results demonstrate successful contact-seeking behaviors in robotic experiments.

Abstract

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.