In order for robots to collaborate efficiently and effectively with humans, the human perception of their movement must be considered in motion creation. Because a human collaborator will interpret the movements of a robot (even subconsciously), robot motion synthesis algorithms that do not consider the human observer may create motions that are perceived incorrectly, interpreted negatively (e.g. as being angry or threatening), or at least miss out on the opportunity to use this subtle communication channel effectively. The key idea of this project is to develop an understanding of human perception of movement that can be applied to the development of robot trajectory planning and control algorithms. The team will use human subjects experiments to understand and evaluate the interpretation of movements and apply these findings in robotics and motion synthesis. The research plan interleaves empirical studies of how people interpret motions, algorithm development to create methods that generate robot motions in a controllable manner, and contextualized deployments that allow the PIs to evaluate the success of the methods. The success of the project will provide a deeper understanding of how people interpret movements, new algorithms for synthesizing robot movements, and demonstrations of the potential applications of collaborative robots.
Broader Impact: Perceptually inspired robot motion synthesis algorithms will enable robots to collaborate more effectively with people. It will enable more communicative robots that can serve as teachers and guides; more approachable and acceptable robots that can work in domestic situations such as elder care; more cooperative robots that can work as assistants to workers; and easier to instruct robots that can be trained by non-experts. This project will enhance the education and outreach efforts of hte PIs by connecting empirical human studies to the technical challenges of robot trajectory planning.
