In this talk, we present an end-to-end approach to transform multi-modal tactile signals into a compliant control to generate different dynamic robot behaviors. This is obtained by fusing multi-modal sensor signals from our robot skin and joint sensors with different control approaches. One advantage of these compliant behaviors is to produce safer robots, especially for physical Human-Robot Interaction. A key component of our framework is a robot parametric modeling based on the information of the robot skin multi-modal sensors (proximity, force and acceleration). The obtained models are used to control a robot improving and even changing its dynamic behavior. We validate our framework in our robot TOMM (a wheeled humanoid robot), where our presented framework enables a stiff robotic system to be compliant and react to multi-modal tactile events (pre-contacts and contacts).
Dr. Emmanuel Dean received the M.Sc.and Ph.D. degrees in Mechatronics from the Center for Research and Advanced Studies, Mexico City, Mexico, in 2003 and 2006, respectively. Since 2013, he has been a Senior Researcher with the Chair for Cognitive Systems, Technical University of Munich, Munich, Germany. His research interests include robotics, low-level control, and physical human–robot-interaction/collaboration.