Physical Sciences and Mathematics Commons^™

Autonomous Robot Skill Acquisition, George D. Konidaris

Open Access Dissertations

Among the most impressive of aspects of human intelligence is skill acquisition—the ability to identify important behavioral components, retain them as skills, refine them through practice, and apply them in new task contexts. Skill acquisition underlies both our ability to choose to spend time and effort to specialize at particular tasks, and our ability to collect and exploit previous experience to become able to solve harder and harder problems over time with less and less cognitive effort.

Hierarchical reinforcement learning provides a theoretical basis for skill acquisition, including principled methods for learning new skills and deploying them during problem solving. …

Corrective Gradient Refinement For Mobile Robot Localization, Joydeep Biswas, Manuela M. Veloso, Brian Coltin

Computer Science Department Faculty Publication Series

Particle filters for mobile robot localization must balance computational requirements and accuracy of localization. Increasing the number of particles in a particle filter improves accuracy, but also increases the computational requirements. Hence, we investigate a different paradigm to better utilize particles than to increase their numbers. To this end, we introduce the Corrective Gradient Refinement (CGR) algorithm that uses the state space gradients of the observation model to improve accuracy while maintaining low computational requirements. We develop an observation model for mobile robot localization using point cloud sensors (LIDAR and depth cameras) with vector maps. This observation model is then …