The goal of this National Robotics Initiative (NRI) project is to enable mixed teams of humans and robots to work together to accomplish physically demanding object manipulation tasks in complex environments. For this project, soft robots are exclusively considered, because traditional robots are too heavy and potentially dangerous to work closely with people. Humans can effectively work together to move a bulky, heavy object because they are able to use their understanding of group goals and individual capabilities to interpret physical cues and quickly infer each other's intention. Thus, the first step in extending this ability to robots is to understand how groups of people recognize and react to pushing and pulling from other team members. The project also emphasizes the necessity of managing uncertainty when working with soft robots and with people -- soft robots because they deform significantly under typical task loads, and people because their movements may be difficult for robots to predict. Potential applications of the research can range from expediting logistics and material handling, to improving human safety in dangerous and/or hard-to-reach environments such as mining, oil rigs, logging, and search and rescue. To this end, a collaboration with a local search and rescue team will solicit feedback on human-robot co-manipulation throughout the project. Underrepresented undergraduate students will be trained with a STEM education tool leveraging soft robotics, and the students will then work to disseminate this training to local K-12 classrooms.
Co-manipulation can be defined as the actions taken and the signals sent by many collaborating agents while moving a single large object. This research will enable co-manipulation between humans and robots, and is focused on the following three main thrusts: 1) modeling, controlling, and planning effective stiffness trajectories for soft robots to deal with task uncertainty, 2) quantifying and modeling human intention and consensus during manipulation, and 3) developing algorithms that incorporate intention, consensus, and uncertainty to execute co-manipulation tasks. Building on prior work on model predictive control algorithms for large-degree-of-freedom soft robots, stiffness trajectories will be generated as part of the soft robot control based on estimates of task uncertainty. Trials with human collaborators moving large objects in real life and in virtual reality will allow the development of algorithms that predict consensus and motion of the group. Finally, given a reasonable estimate of the short-term motion goal of a group, the resulting algorithms will also generate robot motion and stiffness trajectories to help a group reach consensus more efficiently by reducing uncertainty. This research will pioneer the novel combination of natural physical interaction, control for safe robots, multi-agent coordination, and planning/acting in a distributed manner.
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.
