Abstract

Even with the substantial progress we have seen in Robot Learning, we are nowhere near general purpose robots that can operate in the real world that we live in. There are two fundamental reasons for this. First, robots need to build concise representations in high-dimensional sensory observations often without access to explicit sources of supervision. Second, unlike standard supervised learning, they will need to solve long-horizon decision making problems. In this talk, I'll propose a recipe for general purpose robot learning that combines ideas of self-supervision for representation learning with ideas in RL, adaptation, and imitation for decision making.

Bio

Lerrel Pinto is an Assistant Professor of Computer Science at NYU. His research interests focus on machine learning and computer vision for robots. He received a PhD degree from CMU in 2019; prior to that he received an MS degree from CMU in 2016, and a B.Tech in Mechanical Engineering from IIT-Guwahati. His work on large-scale robot learning received the Best Student Paper award at ICRA 2016 and a Best Paper finalist award at IROS 2019. Several of his works have been featured in popular media such as The Wall Street Journal, TechCrunch, MIT Tech Review, Wired, and BuzzFeed among others. His recent work can be found on www.lerrelpinto.com.

Location:

In person

Swearingen Engineering Center in Room 2A31

Virtual MS Teams

Time

2:20-3:10pm