3 accepted papers
We present a novel method to learn human preferences during, and for, the execution of concurrent joint humanrobot tasks. We consider tasks realized by a team of a human operator and a robot helper that should adapt to the human's task execution preferences. Different human operators can have differ…
In human-robot collaboration, multi-agent domains, or single-robot manipulation with multiple end-effectors, the activities of the involved parties are naturally concurrent. Such domains are also naturally relational as they involve objects, multiple agents, and models should generalize over objects
This paper presents a novel approach for robot instruction for assembly tasks. We consider that robot programming can be made more efficient, precise and intuitive if we leverage the advantages of complementary approaches such as learning from demonstration, learning from feedback and knowledge tran…