NRI: FND: COLLAB: Coordinating Human-Robot Teams in Uncertain Environments

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The decreasing cost and increasing sophistication of robot hardware is creating new opportunities for teams of robots to be deployed in combination with skilled humans to support and augment labor-intensive and/or dangerous manual work. The vision is for robots to free up time of skilled workers so they can focus on the tasks that they are skilled at (complex problem solving, dextrous manipulation, customer service, etc.) and robots can help with the distracting and frustrating parts of working, such as delivering materials or fetching supplies. This vision is being realized across many sectors of the US economy and abroad, such as in warehouse management, assembly manufacturing, and disaster response. However, progress in this area is being stymied by current methods that are rigid and inflexible, and rely on unrealistic models of human-robot interaction. This project seeks to overcome these problems by proposing new models and methods for teams robots to coordinate with teams humans to complete complex problems. In particular, this project will create and solve realistic models for coordinating teams of humans and robots in uncertain environments. The PIs will investigate innovative approaches to this research area, and will make the following contributions: 1) Enable a transformative re-conceptualization of multi-human multi-robot teamwork the accurately reflects the strengths and limitations of the team, as situated within a temporally dynamic, stochastic environment, 2) develop realistic and general models of human-robot teamwork that consider uncertainty and partial observability, and 3) Contribute innovative and scalable techniques for planning and learning in these models. This research will build off of methods that have been successful in single-robot problems under uncertainty and partially observability: partially observable Markov decision processes (POMDPs). POMDPs model robots and environments, but not humans. However, explicitly including people in these models will be critical in almost all real-world applications. By extending POMDPs to multiple robots interacting with teams of humans, complex and realistic problems with mixed human and robot teams can be represented. The solution methods developed in this project will allow the robots to reason about the uncertainty about the domain and their human teammates, while optimizing their behavior. The methods are broadly applicable to human-robot collaboration domains, but they will be evaluated in an emergency department, an environment with a large amount of uncertainty and many delivery and supply tasks during high-volume times. A team of robots can assist in these tasks. Experiments will take place in simulation and in the UC San Diego Simulation and Training Center with various numbers of humans and robots. The results of this project have the potential to transform the way human-robot coordination is performed.