Abstract: Industry is placing big bets on “brute forcing” robotic control, but such approaches ignore the centrality of resource constraints in robotics on power, compute, time, data, etc. Towards realizing a true engineering discipline of robotics, my research group has been “exploiting and exploring” robot learning: exploiting to push the limits of what can be achieved with today’s prevalent approaches, and “exploring” better design principles for masterful and minimalist robots in the future. As examples of “exploit”, we have trained quadruped robots to perform circus tricks on yoga balls and robot arms to perform household tasks in entirely unseen scenes with unseen objects. As examples of “explore”, we are studying the sensory requirements of robot learners: what sensors do they need and when do they need them during training and task execution? In this talk, I will highlight these examples and discuss some lessons we have learned in our research towards better-engineered robot learners.
Bio: Dinesh Jayaraman is an assistant professor at the University of Pennsylvania’s CIS department and GRASP lab. He leads the Perception, Action, and Learning (Penn PAL) research group, which works at the intersections of computer vision, robotics, and machine learning. Dinesh received his PhD (2017) from UT Austin, before becoming a postdoctoral scholar at UC Berkeley (2017-19). Dinesh’s research has received a Best Paper Award at CORL ’22, a Best Paper Runner-Up Award at ICRA ’18, a Best Application Paper Award at ACCV ’16, the NSF CAREER award ’23, an Amazon Research Award ’21, and been covered in The Economist, TechCrunch, and several other press outlets.
Location: We will meet only virtually. Please use the following zoom meeting information to join us:
Zoom Link: https://illinois.zoom.us/j/83068322043?pwd=McN371foR4WvsLrEIafIVDurVN5kgT.1
Meeting ID: 830 6832 2043
Password: 746892