Knowledge transfer in robot manipulation tasks

Abstract

Technology today has progressed to the point that the true potential of robotics is beginning to be realized. However, programming robots to be robust across varied environments and objectives, in a way that is accessible and intuitive to most users, is still a difficult task. There remain a number of unmet needs. For example, many existing solutions today are proprietary, which makes widespread adoption of a single solution difficult to achieve. Also, most approaches are highly targeted to a specific implementation. But it is not clear that these approaches will generalize to a wider range of problems and applications. To address these issues, we define the Interaction Space, or the space created by the interaction between robots and humans. This space is used to classify relevant existing work, and to conceptualize these unmet needs. GTax, a knowledge transfer framework, is presented as a solution that is able to span the Interaction Space. The framework is based on SysML, a standard used in many different systems, which provides a formalized representation and verification. Through this work, we demonstrate that by generalizing across the Interaction Space, we can simplify robot programming and enable knowledge transfer between processes, systems and application domains.