When robots team up with humans, we want to give human teammates as many possibilities as we can for completing their activities. However, robustly reacting to and controlling for all the different things a human teammate could do is actually very difficult! In this project, we will build on previous work (see attached representative publication) that defines the concept of improvisational teamwork in order to:
- Explore new and existing problem formulations (likely various forms of mixed-integer linear programs) that model the variability and uncertainty that human teammates introduce in collaborative human-robot tasks;
- Develop new algorithms and approaches for reacting to the uncertainty that a human teammate introduces, particularly focusing on providing the human with greater choice and autonomy;
- Gain inspiration and insights from mathematical fields such as geometry, graph theory, and most notably, operations research; and
- Extend or adapt existing temporal plan quality metrics to handle scheduling situations that involve uncertainty, preferences, or choice.
The mission of the HEATlab is to create new techniques for human-robot teaming—the flexible navigation and coordination of complex, inter-related activities in shared spaces. We focus on using ideas from AI to automate the scheduling and coordination of human-robot teams. We are particularly motivated by the challenge of coordinating the activities of human-robot teams in environments that require explicit cooperation to be successful. Our goal is to create human-robot teams that exploit the relative strengths of humans and agents to accomplish what neither can achieve alone.