Abstract
Motivated by transportation tasks on construction sites, this contribution deals with an AI-driven approach to human-robot collaborative transportation. An essential part of the considered problem is navigating the robot to the object to be transported, in the presence of other moving items like the human moving to the object. Robot navigation is tackled with reinforcement learning, and the impact of randomness in the other moving items’ behaviour on the robot's training performance is investigated. Results show that the move failure rate of the trained robot policy increases, when the behavioural patterns in the human's movements are disturbed by randomness. On the other hand, when both human and robot are connected to the object, the navigation problem is delegated to the human, which guides the compound human-object-robot to the goal location.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
