CAREER: MaLPhySiCS - Machine Learning-assisted Physics-based Simulation and Control of Soft robots

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This Faculty Early Career Development (CAREER) grant will support research that will formulate and experimentally validate numerical tools for modeling of soft robots. Soft robots are typically designed and controlled through a painstaking trial-and-error process involving several prototypes. This project seeks to automate this design and control process using a real-time physics-engine. The simulation addresses several key challenges in simulation of robots: (1) large structural deformation, (2) nontrivial coupling with hydrodynamic forces, and (3) preponderance of contact and collision. Model reduction is necessary to cast a complex robot into the simulation framework. Machine learning (ML) provides an exceptional set of tools to reduce complex structures into a model without compromising accuracy. The fast simulation tool, based on physics and ML, will be used to build a control framework for a bacteria-inspired soft robot.The research objective of this project is formulation of fast and efficient physics-based simulation, assisted by machine learning, for autonomous control of soft robots. Using this framework, a macroscale bacteria-inspired robot will be designed and controlled. This robot will use buckling in flagellum (thin flexible tail) to control its swimming direction. This is expected to be the simplest autonomous soft robot with a single scalar control input. Two key challenges to be tackled in the project are: (1) computational efficiency so that the simulation can be used for optimization, and (2) physical accuracy and robustness of the models so that model-based control can be employed on the real robotic systems. Towards this goal, machine learning-assisted modeling of complex systems in a discrete differential geometry-based simulation framework is planned. Neural network-based models for the structure of the robot and the hydrodynamics will be developed. These models are expected to be as fast as simplified heuristic models and as accurate as physics-based fine-grained models. This simulation tool will be used to develop a model-based control framework for the bacteria-inspired robot for untethered autonomous operation. This robot can help us gain insight into bacterial locomotion, e.g., role of instability in bacterial propulsion. From a robotics perspective, the robot has only one control input with minuscule number of moving parts. The design of the robot makes it amenable for miniaturization to sub-millimeter scale with potential biomedical applications.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.