NSF CAREER: Making Robots More Cooperative Agents: Controlling Costs of Coordination Through Graph-Based Models of Joint Activity


The deployment of smart robots promises increased safety, productivity, and capability in domains such as disaster and emergency response, ground mobility, manufacturing, aviation, and space operations. Good human-robot collaboration is key to the realization of these promises. This project develops novel modeling techniques for analyzing and designing collaborative behavior in human-robot teams. Collaborative behavior requires adjusting and communicating with each other, thereby benefiting from participating in collaboration despite the cognitive and temporal costs of needing to coordinate with others. Such costs in human-robot collaboration can be high, as coordination with autonomous agents generally is more taxing and time-consuming than collaboration with other humans. The models developed as part of the award will help uncover the causes and effects of coordination costs in human-robot systems. Based on these models, the project develops techniques for managing coordination costs to avoid overloading human operators. Improved management of coordination costs will lead to more robust and resilient human-robot operations, broader adoption of smart robotic technologies, and realization of their promised benefits in a range of domains that are key to social welfare and national security. The project integrates education and outreach activities into the research for training the future workforce in systems thinking and interdisciplinary problem-solving skills. These skills will ready future engineers, researchers, and scientists to create integrated solutions in interdisciplinary environments to address complex engineering challenges that span technological, human, ecological, economic, and policy dimensions, among others.

The research develops a generalizable formalization for representing and analyzing joint activity in human-robot systems by combining theories from cognitive and social sciences with techniques from graph theory and agent-based modeling. This framework affords objective and dynamic analysis of the teamwork required to manage interdependencies between humans and robots. Based on the model, the research develops techniques for dynamically adapting and controlling coordination costs to improve collaboration and avoid coordination breakdowns. The work will be validated in disaster response and space operations. The project addresses three fundamental research challenges: First, it determines the relation between a human-robot system’s organization, asymmetries in cooperative competencies, and cognitive and temporal costs of coordination with robots. Second, it identifies control strategies for dynamically regulating coordination costs in human-robot systems. Third, it demonstrates the use of graph-theoretical metrics and algorithms to translate theoretical concepts of joint activity into actionable guidance for making robots more cooperative agents in dynamic environments. Findings will provide deep insight into what capabilities robots need to be endowed with to make them useful cooperative agents in context, with implications for how robotic functionality should be deployed to improve the robustness and resilience of complex operations.


Martijn IJtsma

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