Dynamic Shared Control for Soft Robots

This award supports research on human-robot collaboration while controlling soft robots made of flexible or stretchable materials. Soft robots are well-suited for real-world tasks and can interact with humans more safely and robustly than traditional robots. However, predicting humans' actions and reactions to operate can be challenging for the robot, while direct control of the robot by humans may, in turn, require an intuitive understanding of the robot's movements, increasing the workload for the user. The research team will explore the potential of shared control, which can help soft robots benefit from human insights into environmental interactions, while also reducing the burden on the human user. The findings of this research will be instrumental in advancing the integration of soft robots into a range of human environments, from personal to industrial, and could lead to new emergent behaviors that improve our autonomous control of these robots. The project will also reach out to the local community and high school students to increase engagement and awareness of STEM disciplines and human-robot interaction. This research will advance fundamental understanding of human teleoperation of soft robotic systems and the emergent behaviors that arise when control is shared in task-responsive ways. First, real-time features of user workload in the operation of systems with complex passive and active movements will be investigated, giving a better understanding of how tool (robot) features affect the operator's workload. This will provide a first-ever measure of the relationship between the human impression of soft robots and the workload when using a novel robot. Second, mechanical models of soft robot limits to predict buckling and material failure events, providing safer operations for autonomous and teleoperated systems will be developed. Third, the project will investigate how communication of limits affects human operator behavior and soft robot performance. Lastly, sliding shared control algorithms for soft robots will be developed, leading to increased performance of the systems and better adoption as tools for human operators. 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.