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This lecture explores learning models for solving belief-driven mobile manipulation tasks in open environments. It covers topics such as continuous error checking, recovery, and actions like leaping, grasping, dragging, climbing, throwing, and stacking. The instructor presents a technical approach involving Aspect Transition Graphs, Active Belief Planner, and Landscape of Attractors. Additionally, the lecture discusses Intrinsically Motivated Structure Learning, habituation thresholds, and examples of stacking experiments.