In recent years, we have seen a shift in different fields of AI such as computer vision, robotics. From task-driven supervised learning, we are now starting to see shift towards more human like learning. Self-supervised learning, embodied AI, multimodal learning are few subfields which have emerged from this shift. Yet I will argue the shift is half-hearted in nature and there is a huge situational gap between babies (human learners) and current robots. Our babies use five senses, multiple environments and rich forms of supervision. On the other hand, our AI algorithms still primarily use vision (best case), learn for pre-defined tasks and use categories as supervision. In this talk, I will argue how to bridge this gap. First, I will talk about how to bring tactile sensing into mainstream. More specifically, I will introduce our magnetic sensing skin called ReSkin. Next I will talk about how to formulate task-agnostic learning to learn from multiple environments. Finally, I will talk about using human functions as supervision to train representations.

Bio:

Abhinav Gupta is an Associate Professor at the Robotics Institute, Carnegie Mellon University and Research Manager at Facebook AI Research (FAIR). His research focuses on scaling up learning by building self-supervised, lifelong and interactive learning systems. Specifically, he is interested in how self-supervised systems can effectively use data to learn visual representation, common sense and representation for actions in robots. Abhinav is a recipient of several awards including IAPR 2020 JK Aggarwal Prize, PAMI 2016 Young Researcher Award, ONR Young Investigator Award, Sloan Research Fellowship, Okawa Foundation Grant, Bosch Young Faculty Fellowship, YPO Fellowship,  IJCAI Early Career Spotlight, ICRA Best Student Paper award, and the ECCV Best Paper Runner-up Award. His research has also been featured in Newsweek, BBC, Wall Street Journal, Wired and Slashdot.

Host:  Xiaolong Wang