Efficient Algorithms for Integrated Task and Motion Planning with Factored Optimization, Sampling, and Learning

This thesis presents novel algorithms that efficiently solve complex Task and Motion Planning (TAMP) problems by tightly integrating discrete task planning with continuous trajectory optimization, adaptively combining sampling and optimization methods, and accelerating computations using deep learning.