The objective of this Faculty Early Career Development (CAREER) project is to identify and test new scientific principles to design swarm robotic systems with dependable collective behavior. Motivated by observations of phenomenal cooperative behavior in nature, large teams of simple robots promise unprecedented task efficiency and resilience benefits over sophisticated standalone systems. These beneficial capabilities are fundamental to the future of disaster response, environment monitoring, military operations and space exploration, and to the continuing scientific leadership of the U.S. in these domains. However, most existing approaches to designing the behavior of mobile robots that operate as large collectives suffer from two key limitations: the lack of predictable performance guarantees at the swarm level and the inability to adapt to different uncertain environments and scales of operation. This award supports theoretical research to concurrently tackle these limitations by leveraging machine learning tools combined with rigorous engineering design approaches that enable systematic analysis and tailoring of how knowledge representation and the physical design of individual robots influence their collective behavior. These theoretical contributions will be reduced to practice by designing and testing new small aerial and ground robots for swarm applications with broad societal impact in the areas of time-critical emergency response and pollution clean-up. Outreach efforts engaging local emergency-response stakeholders will allow understanding of potential barriers to transitioning such swarm-robotic technologies to practice. This multidisciplinary project will also enable novel experiential learning environments and diversity initiatives for engineering students at the intersecting fields of design and robotics, and facilitate advanced skill development in these fields by enriching the graduate curricula and organizing a new workshop at a flagship Robotics conference.
The overarching goal of this research is to investigate the central hypothesis that dependable swarm systems can be computationally designed via imitation learning of individual agent behavior from provably-optimal expert solutions and concurrent tailoring of agent morphology. Here "dependability" encompasses the generalizability, scalability and mathematical explainability of the ensuing collective behavior, which will be analyzed in the context of decentralized swarm robotic systems, comprising palm-sized wheeled robots and multirotor drones, that are tasked to provide target search and collective transport operations. To accomplish this goal, the following three key fundamental contributions are envisioned in this research: 1) develop learnable scale-agnostic representations of the individual agent's knowledge that regulates its task-planning processes embodied by novel Bayesian search and graph-theoretic models; 2) identify hybrid imitation learning approaches for adapting the individual agent behavior over varying environments, while minimizing the deviation of the ensuing collective behavior from provably-optimal offline solutions; 3) develop computational methods based on novel constrained policy gradient and co-evolution approaches to concurrently design agent morphology along with the learning of agent behavior, such that the ensuing morphological complexity optimally facilitates the necessary behavioral adaptations. These contributions will provide an increased understanding of "dependability" and the "interplay of form and behavior" that is expected to impact a broad range of multi-agent and decentralized systems beyond swarm robotics, such as various cyber physical systems. The integrated education plan involves the creation of 1) new experiential learning programs for engineering students, including underrepresented minorities, based on swarm computer games and conservation-focused drone flight experiments, and 2) a new graduate course and conference workshops.
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.
