Task Generalization with Pathwise Conditioning of Gaussian Process for Learning from Demonstration

Abstract

To effectively operate in human-centered environments, robots must possess the capability to rapidly adapt to novel and changing situations. Techniques such as Learning from Demonstration enable fast learning without the need for explicit coding. However, in certain cases they exhibit limitations in generalizing beyond the set of demonstrations, which constrains their ability to rapidly adapt to unforeseen scenarios. In this work, we present a movement primitive learning algorithm based on Gaussian Processes, combined with a zero-shot adaptation to new via-points without requiring retraining, through Pathwise Conditioning. The algorithm not only learns the movement policy but is also capable of adapting it rapidly while preserving prior knowledge. The method has been evaluated through comparisons against other state-of-the-art approaches, experiments in simulated environments, as well as on a real robotic platform, generating new solutions for learned tasks by modifying via-points in both position and orientation.