NRI: Collaborative Goal and Policy Learning from Human Operators of Construction Co¬-Robots

Oklahoma State University, Stillwater OK

Investigators

Abstract

The overall goal of this research is to investigate and significantly advance the science of collaborative interaction between human operators and co-robots. This includes the development of algorithms that can be used to train co-robots from skilled human operators to efficiently perform complex tasks in the face of real-world uncertainty, and to guide novice operators in performing such tasks. The primary targeted application is the construction and farming equipment industry that includes complex co-robots such as excavators, wheel loaders, tractors, forage harvesters where there is a significant need to understand and improve human-robot collaborative learning. There are significant scientific challenges in developing efficient algorithms for co-robots that can actively learn from skilled human operators by observing and posing appropriate queries to close the feedback loop between the co-robot and the human operator. This project addresses these challenges by systematically formulating and investigating focused problems to create efficient algorithms that can enhance collaborative human-robot learning. To achieve the goal, algorithms are designed to collaboratively learn latent subgoal structures from ill-defined complex tasks, real-time path planning and control algorithms are developed for co-robots to achieve the learned subgoals, and techniques are developed to provide operator skill specific task decomposition and motion execution guidance. In addition, the developed algorithms are corroborated by simulators, hardware experimentation on laboratory and field co-robots, and theoretical analysis.