Speaker: Roberto Martin-Martin, University of Texas at Austin
Time: 1:30pm - 2:50pm, Monday Dec 5, 2022
Location: CS 104

Abstract:: Latest advances in robot learning are creating more capable and robust embodied agents, making us consider a future where robots could help humans in their everyday lives. But, what exactly do we want robots to do for us? In this talk, I will consider this question and discuss the results of a first analysis: a new robot benchmark with a human-guided set of tasks in ecological household environments. This benchmark demands solutions with better capabilities for long-horizon planning and control; I will present our first steps in robot perception towards improved planning and control: perceiving human plans to long-horizon tasks from instructional videos, and predicting video to enable versatile visuomotor control.

Bio: Roberto Martin-Martin is Assistant Professor of Computer Science at University of Texas at Austin. His research connects robotics, computer vision and machine learning. He studies and develops novel AI algorithms that enable robots to perform tasks in human uncontrolled environments such as homes and offices. He creates novel decision-making solutions based on reinforcement learning, imitation learning, planning and control, and explores topics in robot-perception such as pose estimation and tracking, video prediction and parsing. Martin-Martin received his Ph.D. from Berlin Institute of Technology (TUB) prior to a postdoctoral position at the Stanford Vision and Learning Lab. His work has been selected as RSS Best Systems Paper Award, RSS Pioneer, Winner of the Amazon Picking Challenge, and ICRA and IROS Best Paper Nominee. He is chair of the IEEE Technical Committee in Mobile Manipulation.