Prof. Sethu Vijayakumar, University of Edinburgh, UK, will give a plenary session with the title: "Machine Learning for Robotics and Sensorimotor Control"
Humans and other biological systems are very adept at performing fast, complicated control tasks in spite of large sensorimotor delays while being fairly robust to noise and perturbations. There are various components involved in achieving such levels of robustness, accuracy and safety in anthropomorphic robotic systems. Broadly, speaking challenges lie in the domain of robust sensing, flexible planning, appropriate representation and learning dynamics under various contexts. Statistical Machine Learning provides ideal tools to deal with these challenges, especially in tackling issues like partial observability, noise, redundancy resolution and scalability.
I will talk about some of the large scale machine learning techniques we have developed for: (a) real time acquisition of non-linear dynamics in a data driven manner, (b) automatic low-dimensional (latent space) representation of complex movement policies and trajectories and (c) planning methods capable of dealing with redundancy (e.g. variable impedance) and adaptation.
Exciting videos of learning in high dimensional movement systems like anthropomorphic limbs (KUKA robot arm, SARCOS dexterous arm, Touch Bionics iLIMB etc.) and humanoid robots (HONDA ASIMO, DB) will serve to validate the effectiveness of these machine learning techniques in real world applications.
Short Bio: Prof. Sethu Vijayakumar is the Director of the Institute for Perception, Action and Behavior (IPAB) at the School of Informatics at the University of Edinburgh, UK. Since August 2007, he holds a Senior Research Fellowship of the Royal Academy of Engineering, co-funded by Microsoft Research. He also holds additional appointments as an Adjunct Faculty at the University of Southern California (USC) and as a Visiting Research Scientist at the RIKEN Brain Science Institute, Japan. His research interest spans a broad interdisciplinary curriculum involving basic research in the fields of statistical machine learning, motor control, supervised learning in connectionist models and computational neuroscience. Prof. Vijayakumar has pioneered the use of large scale machine learning techniques in the real time control of large degree of freedom anthropomorphic robotic systems including the SARCOS and the HONDA ASIMO humanoid robots, KUKA-DLR robot arm and Nao mini-humanoids. He is the author of over 100 peer reviewed publications in these fields, the winner of the IEEE Vincent Bendix award, the Japanese Monbusho fellowship besides serving on numerous EU and NSF grant review panels and program committees of leading machine learning and robotics conferences (http://homepages.inf.ed.ac.uk/svijayak/).
You can download the program from here.