How can a mobile-manipulation robot operate robustly and flexibly in complicated uncertain environments, such as households and disaster zones.
The fields of AI and robotics have made great improvements inmany individual subfields, including in motion planning, symbolic planning, reasoning under uncertainty, perception, and learning. Our goal is to develop an integrated approach to solving very large problems that are hopelessly intractable to solve optimally. We make a number of approximations during planning, including serializing subtasks, factoring distributions, and determinizing stochastic dynamics, but regain robustness and effectiveness through a continuous state-estimation and replanning process. We are applying these ideas to an end-to-end mobile manipulation system, and are engaged in work on improving correctness and efficiency through learning.