Super-Flexible Skin Sensors Embedded on the Whole Body, Self-Organizing Based on Haptic Interactions

As robots become more ubiquitous in our daily lives, humans and robots are working in ever-closer physical proximity to each other. These close physical distances change the nature of human robot interaction considerably. First, it becomes more important to consider safety, in case robots accidentally touch (or hit) the humans. Second, touch (or haptic) feedback from humans can be a useful additional channel for communication, and is a particularly natural one for humans to utilize. Covering the whole robot body with malleable tactile sensors can help to address the safety issues while providing a new communication interface. First, soft, compliant surfaces are less dangerous in the event of accidental human contact. Second, flexible sensors are capable of distinguishing many different types of touch (e.g., hard v.s. gentle stroking). Since soft skin on a robot tends to invite humans to engage in even more touch interactions, it is doubly important that the robot can process haptic feedback from humans. In this paper, we discuss attempts to solve some of the difficult new technical and information processing challenges presented by flexible touch sensitive skin. Our approach is based on a method for sensors to self-organize into sensor banks for classification of touch interactions. This is useful for distributed processing and helps to reduce the maintenance problems of manually configuring large numbers of sensors. We found that using sparse sensor banks containing as little as 15% of the full sensor set it is possible to classify interaction scenarios with accuracy up to 80% in a 15-way forced choice task. Visualization of the learned subspaces shows that, for many categories of touch interactions, the learned sensor banks are composed mainly of physically local sensor groups. These results are promising and suggest that our proposed method can be effectively used for automatic analysis of touch behaviors in more complex tasks.

[1]  Benjamin Kuipers,et al.  Map Learning with Uninterpreted Sensors and Effectors , 1995, Artif. Intell..

[2]  Hiroshi Ishiguro,et al.  State Space Construction by Attention Control , 1999, IJCAI.

[3]  Makoto Shimojo,et al.  Development of a mixed signal LSI for tactile data processing , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[4]  Zengxi Pan,et al.  A flexible full-body tactile sensor of low cost and minimal connections , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[5]  Kazuhiko Shinozawa,et al.  Recognizing human touching behaviors using a haptic interface for a pet-robot , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[6]  Shigeki Sugano,et al.  Whole-body covering tactile interface for human robot coordination , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[7]  Hiroshi Ishiguro,et al.  Haptic Communication Between Humans and Robots , 2005, ISRR.

[8]  Xinyu Wang,et al.  Two-dimensional communication technology inspired by robot skin , 2004, IEEE Conference on Robotics and Automation, 2004. TExCRA Technical Exhibition Based..

[9]  Shigeki Sugano,et al.  Force detectable surface covers for humanoid robots , 2001, 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Proceedings (Cat. No.01TH8556).

[10]  Takayuki Kanda,et al.  Interactive Humanoid Robots for a Science Museum , 2007, IEEE Intell. Syst..

[11]  Arne Jönsson,et al.  Wizard of Oz studies: why and how , 1993, IUI '93.

[12]  Patrick Ragert,et al.  Improving human haptic performance in normal and impaired human populations through unattended activation-based learning , 2005, TAP.

[13]  W. Torgerson.  Multidimensional scaling: I. Theory and method , 1952 .

[14]  Hiroshi Ishiguro,et al.  Map acquisition and classification of haptic interaction using cross correlation between distributed tactile sensors on the whole body surface , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Hiroshi Ishiguro,et al.  Detecting Feature of Haptic Interaction Based on Distributed Tactile Sensor Network on Whole Body , 2007, J. Robotics Mechatronics.

[16]  Masayuki Inaba,et al.  A full-body tactile sensor suit using electrically conductive fabric and strings , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[17]  Yasuo Kuniyoshi,et al.  From Humanoid Embodiment to Theory of Mind , 2003, Embodied Artificial Intelligence.

[18]  Shigeaki Watanabe,et al.  Subspace method to pattern recognition , 1973 .