Mark Moll Ph.D. | Director of Research, PickNik Robotics
Abstract: Robot manipulators are increasingly deployed outside of carefully controlled factory settings. Advances in robot motion planning have made it possible to compute feasible motions for more complex systems. My work is focused on enabling planning over varying time horizons subject to complex soft and hard constraints. The goal is to reduce the amount of user input required to command a robot and enable ever greater levels of autonomy. In this presentation I will first give a brief overview of sampling-based motion planning, a class of methods that has been successfully applied to a broad range of complex systems. I will present recent results that show that satisfying hard constraints can be decoupled from the particular planning strategy, which can lead to surprising performance improvements. Next, I will present some results on using hyperparameter optimization to select and tune motion planning algorithms for a given robot. Finally, will present some initial results on supervised autonomy that combines motion planning with compliant control, perception, and human input.
Bio: Mark Moll received an M.S. in Computer Science from the University of Twente in the Netherlands and a Ph.D. in Computer Science from Carnegie Mellon University. Mark Moll is currently the Director of Research at PickNik, a robotics software development and consultancy company that is supporting the MoveIt motion planning framework. Previously, he was a senior research scientist in the Computer Science Department at Rice University. He has worked in robotics for more than 20 years, with a focus on motion planning. He has led the development of the Open Motion Planning Library (OMPL), which is widely used in industry and academic research (often via MoveIt / ROS). He has over 80 peer-reviewed publications with research contributions in applied algorithms for problems in robotics and computational structural biology. He has extensive experience deploying novel algorithms on a variety of robotic platforms, ranging from NASA’s Robonaut 2 to autonomous underwater vehicles and self-reconfigurable robots.