NRI: Collaborative Research: A Dynamic Bayesian Approach to Real Time Estimation and Filtering in Grasp Acquisition and Other Contact Tasks

A current weakness of robots is their inability to quickly and reliably perform contact tasks in unstructured environments. The goal of this project, which represents a collaboration between faculty at two partner institutions, is to alleviate this shortcoming by developing techniques that will afford robots accurate real-time perception in tasks exhibiting intermittent contact. Project outcomes will have a strong impact in manipulation tasks, as robots become more capable and autonomous. The PIs also expect successful applications in other areas, for instance to drive real-time haptic displays in augmented reality systems, to extract human manipulation strategies from observed kinesthetic demonstrations, and to identify model parameters to improve simulation accuracy, not to mention in advancing the level of autonomy for space and undersea exploration. Additional applications outside of robotics are anticipated in situations where a system experiences abrupt state transitions and the goal is either state estimation or real-time feedback control (e.g., chemical, financial, and geological systems). The PIs' labs have a track record of supporting women and under-represented minorities, and the research will be integrated into a variety of pedagogical activities at the graduate and undergraduate level on both campuses. In previous work the team proposed the DBC-SLAM framework, in which continuous states (i.e., poses, velocities and contact impulses), and discrete contact states (i.e., contact-noncontact and stick-slip) of the manipulated objects, are tracked and important model parameters are estimated. In this research, they will extend that work significantly in two directions. First, they will design new parallel, anytime complementarity problem (CP) solvers in order to attain real-time performance. Second, they will enhance the dynamic Bayesian models in DBC-SLAM to allow the use of point-cloud observations and more complex geometric models of the objects, robot links, and environment. The intellectual merit of the project lies in three main activities: first, the creative, yet rigorous, technical process of designing perception algorithms based on fundamental first principles of nonsmooth mechanics and Bayesian estimation in a way that can utilize point-cloud data; second, achieving real-time performance by exploiting the mathematical structure and properties of both the nonsmooth multibody dynamics and CPU/GPU computing systems; and third, pursuing the first two activities in a way that sheds light on the trade-offs between estimation accuracy and speed.