NSF Award
1939930
Soft robots are a compelling new platform for operating alongside humans in unstructured, rugged, and dynamic environments. However, as of yet, very few soft robots are field-deployable in scenarios such as search-and-rescue and disaster response. This is due in part to the challenge of finding ways of making soft robots move effectively. The central aim of this project is to establish methods by which soft robots can autonomously develop environment-specific task repertoires with little or no prior knowledge about their own abilities or the surrounding environment. These new techniques will allow robots to quickly and efficiently retrain themselves when they are damaged or when their task environment changes. Importantly, this work will also establish a model for involving and developing undergraduate students as independent researchers in the high risk, high payoff field of soft robotics, thereby growing the community of researchers and lowering the barriers of entry for the next generation of robotics researchers.<br/><br/>Specifically, this project will use of Quality Diversity Algorithms to efficiently and autonomously discover effective soft robotic behaviors that allow them to robustly and adaptively move in complex environments. These techniques will be developed using low-cost dynamically complex tensegrity-based robots. The specific goals of this research are to produce insights into how soft robots can autonomously explore the range of their abilities, producing multimodal repertoires of behaviors that fully leverage their dynamics, and to develop methods by which these robots can robustly and efficiently adapt their repertoires in response to damage and unexpected environmental change. Throughout, this effort will involve substantial hardware-based validation and testing using a high speed, high resolution motion capture system.<br/><br/>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.
