Thesis Defense: Advancing Dexterous Manipulation via Machine Learning

Abstract: Robots are becoming better at navigating and moving around, but they still struggle with using tools, which severely limits their usefulness for household tasks. Using tools requires dexterously manipulating everyday objects like hammers, scissors, knives, screwdrivers, etc. While simple for humans, manipulating everyday objects remains a long-standing challenge that requires breakthroughs in robotic hardware, sensing, perception, and control algorithms. This talk introduces machine learning techniques that substantially improve the state-of-the-art performance of dexterous manipulation controllers. It focuses specifically on in-hand object reorientation tasks. Previous works on this problem had limitations like using expensive sensors or hands, only working for a few objects, requiring the hand to face upward, slow object motion, etc. This talk discusses how we can go a step further by enabling a low-cost robot hand to dynamically reorient diverse objects in mid-air with the hand facing downward using an inexpensive depth camera.

Bio: Tao Chen is a Ph.D. student advised by Prof. Pulkit Agrawal in Improbable AI lab at MIT CSAIL. His research focuses on robot learning, in particular, dexterous manipulation in robotics. He has received the Best Paper Award at the top robot learning conference, CoRL 2021, and has also published in the Science Robotics journal.

Thesis committee: Pulkit Agrawal, Daniela Rus, Russ Tedrake