5th Workshop on
Semantic Policy and Action Representations for Autonomous Robots (SPAR)
September 27, 2021 - Prague, Czech Republic
at IROS 2021
Oliver Kroemer
Learning to Structure Manipulation Skills
In the future, we want to create robots with the robustness and versatility to operate in unstructured and everyday environments. To achieve this goal, robots will need to learn manipulation skills that can be applied to a wide range of objects and task scenarios. In this talk, I will be presenting recent work from my lab on structuring manipulation tasks for more efficient learning. I will discuss how modularity can be used to break down challenging manipulation tasks to learn general object-centric solutions.
Oliver Kroemer received the bachelor's and master's degrees in engineering from the University of Cambridge, Cambridge, U.K., in 2008, and the Ph.D. degree in computer science from the Technische Universitaet Darmstadt, Darmstadt, Germany, in 2014.,He was a Postdoctoral Researcher with the University of Southern California (USC), Los Angeles, CA, USA, for two and a half years. He is currently an Assistant Professor with the Robotics Institute, Carnegie Mellon University (CMU), Pittsburgh, PA, USA, where he leads the Intelligent Autonomous Manipulation Lab. His research focuses on developing algorithms and representations to enable robots to learn versatile and robust manipulation skills. [1]