Sequential clustering for tactile image compression to enable direct adaptive feedback The sense of touch is often crucial for humans to perform manipulation tasks. Providing tactile feedback during teleoperation or for users of prosthetic devices would be beneficial. However, the representation of tactile information constitutes a major technical challenge, since the numerous and possibly multimodal sensor readings are massive compared to the available tactile display technology. We introduce an algorithm that deploys two stages of Kmeans clustering along and across tactile image frames that render tactile sensor information at each time instant. In this manner, the massive tactile information is adaptively compressed in realtime while preserving its physical meaning, thus, remains intuitive and direct. We experimentally verify and examine the characteristics of our algorithm by evaluating the original and compressed tactile data. The data was gathered during the active tactile exploration of several objects of daily living by an Allegro robot hand that was covered with 15 uSkin sensor modules providing 2403axis force vector measurements at each time instant. Our novel algorithm is straight forward enough to be implemented into tactile feedback systems. Finally, our algorithm allows for the direct feedback of massive tactile sensor data for a broad variety of tactile sensors and tactile displays, thereby, enables the compressed yet intuitive representation of massive tactile sensor information for realtime applications.

Compensating for Fingertip Size to Render Tactile Cues More Accurately Fingertip haptic feedback offers advantages in many applications , including robotic teleoperation, gaming, and training. However, fingertip size and shape vary significantly across humans, making it difficult to design fingertip interfaces and rendering techniques suitable for everyone. This paper starts with an existing datadriven haptic rendering algorithm that ignores fingertip size, and it then develops two softwarebased approaches to personalize this algorithm for fingertips of different sizes using either additional data or geometry. We evaluate our algorithms in the rendering of prerecorded tactile sensations onto rubber casts of six different fingertips as well as onto the real fingertips of 13 human participants. Results on the casts show that both approaches significantly improve performance, reducing force error magnitudes by an average of 78% with respect to the standard nonpersonalized rendering technique. Congruent results were obtained for real fingertips, with subjects rating each of the two personalized rendering techniques significantly better than the standard nonpersonalized method.