SGER: Hierarchical Knowledge Representation in Robotics

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

This SGER proposal concerns the accumulation and representation of skills and control knowledge by robots that interact with unstructured environments. There has been comparatively little work on representations that capture re-useable knowledge in robotics---an issue that lies at the heart of many future applications. Thus, this SGER represents a potentially transformative technology and addresses significant gaps in the state-of-the-art for which the payoff, despite the risk, is extremely high. We aim our 1 year study on learning techniques that accumulate knowledge related to grasping and manipulation. We shall extend pilot studies and build prototypes for self-motivated learning techniques and generative models for manipulation and multi-body contact relationships. The approach relies on learning to discover and exploit structure over the course of several staged learning episodes; from sensory and motor knowledge concerning the robot itself, to controllable relationships between the robot and external bodies, to multi-body contacts involved in tasks like stacking and insertion. The project has three principal technological goals: to advance the state-of-the-art of robotic manipulation and knowledge representation; to extend machine learning methods toward intrinsically motivated, cumulative, and hierarchical learning; and to advance computational accounts of the longitudinal processes of sensorimotor and cognitive development in humans and machines.