Surface Perception through Haptic-Auditory Contact Data

Behnam Khojasteh, Yitian Shao, Katherine J. Kuchenbecker

Keywords: Artificial Surface Perception, Haptic-Auditory Sensing, Multimodal Time-Series Classification, Kernel Methods

TL;DR: We introduce a haptic-auditory test bed for artificial texture perception and propose a set of studies with preliminary results.

Abstract: Sliding a finger or tool along a surface generates rich haptic and auditory contact signals that encode properties crucial for manipulation, such as friction and hardness. To engage in contact-rich manipulation, future robots would benefit from having surface-characterization capabilities similar to humans, but the optimal sensing configuration is not yet known. Thus, we developed a test bed for capturing high-quality measurements as a human touches surfaces with different tools: it includes optical motion capture, a force/torque sensor under the surface sample, high-bandwidth accelerometers on the tool and the fingertip, and a high-fidelity microphone. After recording data from three tool diameters and nine surfaces, we describe a surface-classification pipeline that uses the maximum mean discrepancy (MMD) to compare newly gathered data to each surface in our known library. The results achieved under several pipeline variations are compared, and future investigations are outlined.

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