Collaborative goal and policy learning from human operators of construction co-robots

Human operators of real-world co-robots, such as excavator, require extensive experience to skillfully handle these complicated machines in uncertain safetycritical environments. We consider the problem of human-robot collaborative learning and task execution, where efficient human-robot interaction is critical to safely and efficiently accomplish complex tasks in uncertain environments. Our collaborative learning algorithm enables a construction co-robot to learn latent task subgoals from the demonstrations of skilled human operators which can then be used to guide novice human operators in completing complex tasks under uncertainty. The effectiveness our algorithm is demonstrated through experimentation on a scaled model of an excavator with guided and unguided human operators. Our results demonstrate that when the co-robot’s inferred subgoals are communicated back to the novice human operator, task performance significantly improves.