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Tracking the pose of an object while it is being held and manipulated by arobot hand is difficult for vision-based methods due to significant occlusions.Prior works have explored using contact feedback and particle filters tolocalize in-hand objects. However, they have mostly focused on the static graspsetting and not when the object is in motion, as doing so requires modeling ofcomplex contact dynamics. In this work, we propose using GPU-acceleratedparallel robot simulations and derivative-free, sample-based optimizers totrack in-hand object poses with contact feedback during manipulation. We usephysics simulation as the forward model for robot-object interactions, and thealgorithm jointly optimizes for the state and the parameters of thesimulations, so they better match with those of the real world. Our method runsin real-time (30Hz) on a single GPU, and it achieves an average point clouddistance error of 6mm in simulation experiments and 13mm in the real-worldones. View experiment videos athttps://sites.google.com/view/in-hand-object-pose-tracking/

In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation