This project addresses the difficult theoretical, computational, and applied challenges required to exploit a deep mathematical relationship between recent advances in machine perception/estimation algorithms and recent advances in algorithms for planning/controlling systems undergoing frictional contact. It explores the immediate applications of these algorithms to perception, robotic object manipulation and parts assembly, and humanoid robots performing complex, multi-contact, whole-body maneuvers. In order to showcase the generality of approach, and simultaneously reach out to the important under-represented minority population, the research team employs a new hands-on short-course curriculum in which students apply the proposed algorithms to predict the outcome of games using visual tracking. The course is being developed in partnership with the MIT Office of Minority Education (OME).
The project brings together expertise in simultaneous localization and mapping (SLAM), robot manipulation, robotic automation, legged robots, and optimization and nonlinear control, leading to a cross-fertilization of ideas and techniques. The research team exploits sparsity in the complementarity formulations of contact in Lagrangian dynamics. The project explores a new algebraic approach to nonlinear estimator design. The project produces new theorems, new algorithms, and experimental results on real robots. The project also represents a new partnership with our industrial collaborator, ABB Robotics. The developed algorithms facilitate a broad range of new applications in which perception and control systems monitor and manipulate physical interactions with the world. From palm-sized smart devices to environmental monitoring, sensors are becoming ubiquitous; to reach their full potential these sensor networks must be able to reason about contact - the basic building block of physical interaction.
