Computing and Mathematical Sciences Colloquium
Machine Learning and Optimization for Robotics
In this talk I will describe two main ideas. First, I will describe apprenticeship learning, a new approach to equip robots with skills through learning from ensembles of expert human demonstrations. Our initial work in apprenticeship learning enabled the most advanced helicopter aerobatics to-date, including maneuvers such as chaos, tic-tocs, and auto-rotation landings which only exceptional expert human pilots can fly. In our current work we are studying how a robot could learn to perform challenging robotic manipulation tasks, such as knot-tying, cloth manipulation, and assembly.
Second, I will describe advances in belief space planning, where, rather than planning in the original state space, planning is done in the space of probability distributions over states. Optimal plans in belief space do not only plan for actions that affect the state of the system, but also for information gathering actions, which can be essential in the presence of significant uncertainty. While in general such problems are intractable, I will present approximate solutions obtained through Gaussian belief space planning that perform well in practice.
Contact: Carmen Nemer-Sirois at (626) 395-4561 email@example.com