The realization of human-safe robots would present opportunities for the deployment of human-robot teams in complex assembly tasks. Humans can perform complex tasks that require dexterity, while robots can perform supporting tasks that do not require a high degree of dexterity. Having humans and robots operate in close proximity, while utilizing their complementary strengths, can significantly enhance human productivity and reduce job stress for humans. When humans work in teams, it is important for the team members to develop fluent team behavior and the same should hold for a human-robot team. This requires robotic assistants to adapt to the preferences of human teammates, anticipate their actions and support them in performing the task. This award supports fundamental research to enable adaptation of the actions of multiple robots collaborating with a human teammate in an assembly manufacturing task. Results from the research will facilitate introduction of multiple industrial robots in assembly manufacturing tasks and will result in growth opportunities for the US manufacturing industry. The integration of the research with graduate and undergraduate courses will enhance robotics and manufacturing curricula and enrich the learning experiences of the participating students. Outreach activities will educate and inform K-12 students about career opportunities in robotics and manufacturing.
Effective adaptation of multiple robots in human-robot hybrid cells requires fundamental advances in human preference modeling, human action prediction and human-aware task and motion planning. This research will investigate computational foundations for the design of machine learning algorithms that identify the dominant preferences of human operators in canonical assembly tasks. Algorithms will be developed for generating task plans that specify tasks that need to be performed and assigning them to various agents in the system and for predicting the next human action using task plan cues and work cell monitoring. This research will explore and characterize methods for using the predicted actions to adapt the actions of multiple robots interacting with a human teammate on an assembly task.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
