TITLE: Polyglot Robots: Versatile Goal-Based Task Specification for Robot Learning
ABSTRACT: An important goal of the field sensorimotor robot learning is to do away with cumbersome expertise-intensive task specification, so that general-purpose robots of the future might learn large numbers of new skills. In this talk, I will discuss our recent work on algorithms that exploit goals as a versatile and accessible task specification interface. Goals might be specified through images, language, or physical objects, and may either be provided by a layperson or even discovered autonomously by a robot exploring its environment. I will show how unsupervised learning from large human action datasets can train goal-conditioned value functions for robots, how learned verification behaviors can in turn help to evaluate and acquire new skills, and how careful model-based reasoning can help a robot discover interesting goal-based tasks in an environment with no supervision.
Bio: Dinesh Jayaraman is an assistant professor at the University of Pennsylvania's CIS department and GRASP lab. His research group focuses on developing machine learning algorithms that attend to task-relevant components in each stage of the embodied perception-action loop: observation, representation, and action. Dinesh's research has received an NSF CAREER award '23, Best Paper Award at CORL '22, AAAI New Faculty Highlights award '21, an Amazon Research Award '21, a Best Paper Runner-Up Award at ICRA '18, a Best Application Paper Award at ACCV '16, and been featured on the cover page of Science Robotics and in several press outlets. His webpage is at: www.seas.upenn...
15 мар 2023
