Learning Models for Belief-Driven Mobile Manipulation Tasks

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.