Humans act upon their environment through motion, the ability to plan their movements is therefore an essential component of their autonomy. In recent decades, motion planning has been widely studied in robotics and computer graphics. Nevertheless robots still fail to achieve human reactivity and coordination. The need for more efficient motion planning algorithms has been present through out my own research on "human-aware" motion planning, which aims to take the surroundings humans explicitly into account. I believe imitation learning is the key to this particular problem as it allows to learn both, new motion skills and predictive models, two capabilities that are at the heart of "human-aware" robots while simultaneously holding the promise of faster and more reactive motion generation. In this talk I will present my work in this direction.
Biography: Jim Mainprice received his M.S. from Polytech' Montpellier, France, and his Ph.D. in robotics and computer science from the University of Toulouse, France, in 2009 and 2012 respectively. His research interests include motion planning, machine learning, human-robot interaction and human movement understanding. While completing his Ph.D. at LAAS-CNRS, he took part in the European community's 7th framework program projects Dexmart and Saphari. From January 2013 to December 2014 he was a postdoctoral researcher in the Autonomous Robotic Collaboration Lab at the Worcester Polytechnic Institute in Massachusetts, USA, where he participated in the DARPA Robotic Challenge with the DRCHubo team. Since January 2015, he is affiliated with the Max Planck Institute for Intelligent Systems in Tübingen, Germany. Since April 2017, he leads the Humans to Robots Motion (HRM) research group at the University of Stuttgart, Germany, which is part of the "System Mensch" alliance between the Universities of Stuttgart and Tübingen.