Explore an integrated strategy for planning, perception, state-estimation, and action in complex mobile manipulation domains through this lecture by MIT's Tomás Lozano-Pérez. Delve into planning in the belief space of probability distributions over states using hierarchical goal regression. Discover a vocabulary of logical expressions describing sets of belief states and learn how a small set of symbolic operators can generate task-oriented perception for manipulation goals. Witness the implementation of this method in simulation and on a real PR2 robot, demonstrating robust and flexible solutions to mobile manipulation problems involving multiple objects and substantial uncertainty. Gain insights into topics such as POMDPs, complex conditional effects, geometric heuristics, hierarchical planning, belief updates, and motion planning with uncertainty.