Behavior Synthesis via Contact-Aware Fisher Information Maximization

Here, we show emergent tactile behaviors resulting from the proposed contact-aware Fisher information maximization method that results in human-like tactile behaviors for learning (a) mass and weight, (b) friction and textures, (c) stiffness, and (d) shape [20]. 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. Contact dynamics are commonly used in robotics to manipulate the robot itself, e.g., through locomotion, or manipulate objects in its environment. However, the utility of contacts goes beyond just manipulation, and instead, contact can be seen as a medium to transmit information that can help a robot learn about its environment. In fact, prior work has demonstrated the information-richness of contact as a means to improve parameter estimation problems [8, 21, 27]. The underlying challenge is enabling robot behaviors that can actively acquire contact data for learning.