SGER: Constructivist Learning using ASyMTRe in Multi-Robot Teams

University Of Tennessee Knoxville, Knoxville TN

Investigators

Abstract

SGER: Constructivist Learning using ASyMTRe in Multi-Robot Teams Constructivist learning is the process of actively learning new skills based on previous experience. This research project extends the state of the art for constructivist learning in multi-robot teams by developing new, computationally efficient techniques that allow robot team members to continually improve their skills over time. Current approaches to constructivist learning in robotics find correlations between existing low-level robot actions and a desired behavior. However, because these existing approaches begin with such a low level of action abstraction, they are extremely computationally intensive. Our new constructivist learning approach begins at a higher level of abstraction - the sensori-motor schema - which should enable much more computationally efficient learning. Our approach builds upon our prior work, called ASyMTRe, that forms multi-robot coalitions by automatically combining sensori-motor schema building blocks to solve the task at hand. This proposed work adds an important learning component allowing robot team members to continually improve their skills by "chunking" existing low-level schema building blocks into efficient higher-level task solutions. This new approach will provide important new lifelong learning capabilities to multi-robot teams, thus significantly facilitating their use in real-world applications, such as search and rescue, security, mining, hazardous waste cleanup, industrial and household maintenance, automated manufacturing, and construction. We also intend to show that the proposed techniques are applicable to other types of robotic systems, including humanoid and service robots, and thus have a broader impact on the robotics field as a whole.