Behavior Synthesis via Contact-Aware Fisher Information Maximization

2025-05-18

AI Summary

Low information efficiency in robotic learning of object physical parameters—such as mass, friction, and elasticity—through contact interactions limits estimation accuracy and convergence speed. To address this, we propose an information-gain-driven active behavior synthesis framework. Our core contribution is the first formulation of a Fisher information metric tailored to contact dynamics, enabling principled, information-theoretic quantification of how contact data informs parameter estimation. Leveraging this metric, we design a parameter-agnostic, adaptive exploration strategy grounded in optimal experimental design, contact dynamic modeling, and geometric analysis of the Fisher information matrix. The resulting optimization-based behavior synthesis framework generates physically feasible, informative contact actions. Extensive simulations and real-robot experiments demonstrate that our approach significantly improves parameter estimation accuracy (average gain of 32%) and accelerates convergence by 1.8–2.5× compared to baseline methods.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Maximizing contact data utility for robot learning
• Synthesizing behaviors to enhance contact-rich interactions
• Improving object parameter learning via contact-awareness

Innovation

• Active approach maximizes contact data utility
• Contact-aware Fisher information measures behaviors
• Emergent behaviors excite efficient parameter learning