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This study explores using vision-based navigation and adaptive learning systems for robotics. Through practical reinforcement learning, the system leverages human guidance, locally weighted regression, and optical flow to navigate corridors efficiently. A comparison with potential-field methods and empirically derived human control laws is conducted. The research aims to provide insights into phase one and phase two tasks, average training outcomes, and ongoing work on acquiring topological maps.
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Environment Environment Supplied Control Policy Supplied Control Policy Planning Skill Learning Built-in Behaviors Phase One Phase Two R O A R O A Learning System Learning System Adaptive Intelligent Mobile RoboticsLeslie Pack Kaelbling, PIMIT Artificial Intelligence Laboratory Hierarchical Domain Decomposition for Probabilistic Planning • High-level goal determines reward at exit states • Combine pre-computed value functions to determine near-optimal action • Construct decomposition off line • Solve for macro operators • Plan for new goals in time logarithmic in plan length • Trade optimality for efficiency Vision-Based Navigation Practical Reinforcement Learning • Human guidance generates efficient exploration • Locally weighted regression provides fast function approximation • Uncertainty modeling and experience replay cause fast value propagation Optical flow gives estimated distance to objects Comparison of potential-field method to empirically discovered human control laws for local navigation Corridor-following Task Phase 1 Phase 2 Average training “Best” possible Current work: acquiring topological maps based on these primitives
