Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor

We introduce and evaluate contact-based techniques to estimate tactile properties and detect manipulation events using a biomimetic tactile sensor. In particular, we estimate finger forces, and detect and classify slip events. In addition, we present a grip force controller that uses the estimation results to gently pick up objects of various weights and texture. The estimation techniques and the grip controller are experimentally evaluated on a robotic system consisting of Barrett arms and hands. Our results indicate that we are able to accurately estimate forces acting in all directions, detect the incipient slip, and classify slip with over 80% success rate.

[1]  M. Srinivasan,et al.  Tactile detection of slip: surface microgeometry and peripheral neural codes. , 1990, Journal of neurophysiology.

[2]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[3]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[4]  R. Johansson,et al.  Tangential torque effects on the control of grip forces when holding objects with a precision grip. , 1997, Journal of neurophysiology.

[5]  R. Johansson,et al.  Mechanisms for force adjustments to unpredictable frictional changes at individual digits during two-fingered manipulation. , 1998, Journal of neurophysiology.

[6]  Claudio Melchiorri,et al.  Slip detection and control using tactile and force sensors , 2000 .

[7]  Stefan Schaal,et al.  Incremental Online Learning in High Dimensions , 2005, Neural Computation.

[8]  Y. Yao,et al.  On Early Stopping in Gradient Descent Learning , 2007 .

[9]  Veronica J. Santos,et al.  Biomimetic Tactile Sensor Array , 2008, Adv. Robotics.

[10]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.

[11]  Monica Gori,et al.  Better manipulation with human inspired tactile sensing , 2009 .

[12]  G.E. Loeb,et al.  Grip Control Using Biomimetic Tactile Sensing Systems , 2009, IEEE/ASME Transactions on Mechatronics.

[13]  Helge J. Ritter,et al.  Using a Piezo-Resistive Tactile Sensor for Detection of Incipient Slippage , 2010, ISR/ROBOTIK.

[14]  Gerald E. Loeb,et al.  Haptic feature extraction from a biomimetic tactile sensor: Force, contact location and curvature , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.

[15]  Sachin Chitta,et al.  Human-Inspired Robotic Grasp Control With Tactile Sensing , 2011, IEEE Transactions on Robotics.

[16]  Tomonori Yamamoto,et al.  Use of tactile feedback to control exploratory movements to characterize object compliance , 2012, Front. Neurorobot..

[17]  C. Natale,et al.  Tactile sensor for human-like manipulation , 2012, 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob).

[18]  Mark R. Cutkosky,et al.  Slip interface classification through tactile signal coherence , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Dumitru Erhan,et al.  Scalable Object Detection Using Deep Neural Networks , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Gerald E. Loeb,et al.  Multimodal Tactile Sensor , 2014, The Human Hand as an Inspiration for Robot Hand Development.

[21]  Pietro Falco,et al.  Integrated force/tactile sensing: The enabling technology for slipping detection and avoidance , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).