In this work, we will explore what influences human’s perceptions of “team” to design better robotic teammates. In particular, we are interested in looking at how the scheduling of a robot teammate can influence a human teammate’s perception of team fluidity. We will explore how we can adapt existing temporal planning models and methods to interact more seamlessly with how humans execute tasks in teamwork settings. In this project, we will:
- Review existing literature in temporal reasoning for representing the preferences and tendencies of humans;
- Explore how explainable, predictable/transparent, and legible current scheduling methods are for human teammates;
- Extend the lab’s previous exploration of fluidity metrics (see representative publication) in scheduling human-robot teamwork to handle the novel ways that humans introduce uncertainty and contingency into scheduling scenarios;
- Develop new algorithms that are responsive to the novel sources of uncertainty for capturing the types of uncertainty that humans introduce to team activities and react in a way that humans will interpret as fluid and natural; and
- Stretch goal: Evaluate our new and existing approaches for multi-robot / human-robot close collaborative tasks on real robotic platforms (e.g., Sawyer: https://robots.ieee.org/robots/sawyer/).
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.