Event

Lockheed Martin Robotics Seminar: What Matters for Deformable Object Manipulation

Friday, September 20, 2019
2:00 p.m.
2121 JM Patterson
Ania Picard
301 405 4358
appicard@umd.edu

Lockheed Martin Robotics Seminar

What Matters for Deformable Object Manipulation

Dmitry Berenson
Associate Professor
Electrical Engineering and Computer Science 
Robotics Institute
University of Michigan 

Abstract 
Deformable objects such as cables and clothes are ubiquitous in factories, hospitals, and homes. While a great deal of work has investigated the manipulation of rigid objects in these settings, manipulation of deformable objects remains under-explored. One of the key challenges in manipulating deformable objects is selecting a model which is efficient to use in a control loop, especially when an accurate model is not available. Our approach to control uses a set of simple models of the object, determining which model to use at the current time step via a novel Multi-Armed Bandit algorithm that reasons over estimates of model utility. I will also present our work on interleaving planning and control for deformable object manipulation in cluttered environments, again without an accurate model of the object. Our method predicts when a controller will be trapped (e.g., by obstacles) and invokes a planner to bring the object near its goal. The key to making the planning tractable is to avoid simulating the motion of the object, instead only forward-propagating the constraint on overstretching. Finally, we have developed a perception method to track deformable objects despite significant occlusion and I will show an experiment that integrates tracking, control, and motion planning for manipulating cloth.

Host

Mumu Xu

Biography 
Dmitry Berenson is an Associate Professor in Electrical Engineering and Computer Science and the Robotics Institute at the University of Michigan, where he has been since 2016. Before coming to University of Michigan, he was an Assistant Professor at WPI (2012-2016). He received a BS in Electrical Engineering from Cornell University in 2005 and received his Ph.D. degree from the Robotics Institute at Carnegie Mellon University in 2011, where he was supported by an Intel PhD Fellowship. He was also a post-doc at UC Berkeley (2011-2012). He has received the IEEE RAS Early Career Award and the NSF CAREER award. His current research focuses on robotic manipulation, robot learning, and motion planning.