On Monday 18 March 2024 from 11 am to 12:30 pm, it was an honour to welcome Prof. Jitendra Malik at the Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague (CIIRC CTU), Czech Republic.
More information about the lecture:
impact.ciirc.cvut.cz/seminars/...
The lecture was organised by the IMPACT research group (impact.ciirc.cvut.cz) at CIIRC CTU in collaboration with #ELLIS Unit Prague (ellis.ciirc.cvut.cz/) and AICZECHIA (www.aiczechia.cz/).
Funded by the EU under the #ERC Advanced Grant "FRONTIER" of dr. Josef Sivic (Grant agreement ID: 101097822).
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. I believe that the most promising approach for this is to train robot skills in simulation and then transfer them to the real world. I will show multiple examples of skills - legged locomotion (quadruped and humanoid), navigation, and dexterous manipulation such as in-hand rotation and twisting caps off bottles - acquired in this paradigm. Along the way, we developed “Rapid Motor Adaptation”, a method for adaptive control in the framework of deep reinforcement learning. Looking to the future, I believe that there are multiple insights from the development of motor skills in children that are relevant to robotics; I will sketch some examples and partial results. While we are many years away from having robots with the skills of a five year old, progress in the last few years has been remarkable and substantial.
Brief Bio:
Jitendra Malik is the Arthur J. Chick Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He is also part-time Research Scientist Director at Meta. Malik’s research group has worked on many different topics in computer vision, human visual perception, robotics, machine learning and artificial intelligence. 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. His honors include 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 member of the National Academy of Engineering and the National Academy of Sciences, and a fellow of the American Academy of Arts and Sciences.
people.eecs.berkeley.edu/~malik/
scholar.google.com/citations?...
1 апр 2024
