|Dr. Jitendra Malik|
Arthur J. Chick Professor, Department of EECS
University of California Berkeley
September 28, 2023 from 10:30 AM to 12:10 PM
in the Library.
Jitendra Malik was born in Mathura, India in 1960. He received the B.Tech degree in Electrical Engineering from Indian Institute of Technology, Kanpur in 1980 and the PhD degree in Computer Science from Stanford University in 1985. In January 1986, he joined the university of California at Berkeley, where he is currently the Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Sciences. He is also on the faculty of the department of Bioengineering, and the Cognitive Science and Vision Science groups. During 2002-2004 he served as the Chair of the Computer Science Division, and as the Department Chair of EECS during 2004-2006 as well as 2016-2017. In 2018 and 2019, he served as Research Director and Site Lead of Facebook AI Research in Menlo Park.
Prof. Malik’s research group has worked on many different topics in computer vision, computational modeling of human vision, computer graphics and the analysis of biological images. Several well-known concepts and algorithms arose in this research, such as anisotropic diffusion, normalized cuts, high dynamic range imaging, shape contexts and R-CNN. He has mentored more than 70 PhD students and postdoctoral fellows.
He received the gold medal for the best graduating student in Electrical Engineering from IIT Kanpur in 1980 and a Presidential Young Investigator Award in 1989. At UC Berkeley, he was selected for the Diane S. McEntyre Award for Excellence in Teaching in 2000 and a Miller Research Professorship in 2001. He received the Distinguished Alumnus Award from IIT Kanpur in 2008. His publications have received numerous best paper awards, including five test of time awards – the Longuet-Higgins Prize for papers published at CVPR (twice) and the Helmholtz Prize for papers published at ICCV (three times). He received the 2013 IEEE PAMI-TC Distinguished Researcher in Computer Vision Award, the 2014 K.S. Fu Prize from the International Association of Pattern Recognition, the 2016 ACM-AAAI Allen Newell Award, the 2018 IJCAI Award for Research Excellence in AI, and the 2019 IEEE Computer Society Computer Pioneer Award. He is a fellow of the IEEE and the ACM. He is a member of the National Academy of Engineering and the National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences.
Plenary Talk Info:
TITLE: Robots that learn and adapt
ABSTRACT: Deep learning has resulted in remarkable breakthroughs in fields such as speech
recognition, computer vision, natural language processing, and protein structure prediction.
Robotics has proved to be much more challenging as there are no pre-existing repositories of
behavior to draw upon; rather the robot has to learn from its own trial and error in its own
specific body, and it has to generalize and adapt. To make this feasible, we have developed
“Rapid Motor Adaptation”, a novel technique for adaptive control in the framework of deep
reinforcement learning. Using this, we can train robots in simulation and then transfer the skills
directly to robots in the real world. I will show multiple examples – quadruped legged
locomotion, biped locomotion, in-hand rotation, flying quadcopters – of the success of this
approach. I will also show examples of life-long learning in robotics, by continuous adaptation
of perception and action in deployed systems.