Spring 2020 GRASP Seminar Series: Tomas Lozano-Pérez, Massachusetts Institute of Technology, "Learning and Using Composable Robot Skills"

Abstract

We would like to augment the basic abilities of a robot by learning to use new sensorimotor primitives (skills) to enable the solution of complex long-horizon problems. However, solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world?

This talk will describe methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot.   I'll demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems.

Presenter's biography

Tomas Lozano-Perez is currently the School of Engineering Professor in Teaching Excellence at the Massachusetts Institute of Technology (MIT), USA, where he is a member of the Computer Science and Artificial Intelligence Laboratory.  He was a recipient of the 2011 IEEE Robotics Pioneer Award and a 1985 Presidential Young Investigator Award. He is a Fellow of the AAAI, ACM, and IEEE.

 

Location

University of Pennsylvania- GRASP Lab
3330 Walnut Street
Wu & Chen Auditorium Levine 101
Philadelphia, PA 19104
United States

Presenter

Tomas Lozano-Perez