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State Estimation in Contact-Rich Manipulation

This paper introduces a Bayesian state estimator for contact-rich manipulation tasks with application in non-prehensile manipulation, industrial assembly or in-hand localization. The core idea of our approach is to explicitly model both the contact dynamics and a torque-based robot controller as part of the underlying system model. Our approach is capable of estimating the state of movable objects for various robot kinematics and geometries of robots and objects. This includes complex scenarios with multiple robots, multiple objects and articulated objects. We have validated our approach in simulation and on a physical robot. The experiments show that multimodal distributions of six degrees of freedom object poses can be accurately tracked in real-time in a complex manipulation scenario.