Biomechanics-based reaching optimization

摘要

This paper presents a Biomechanics-based Lifelike Reaching Controller (BLRC) to generate lifelike reaching motion. The BLRC employs various reaching strategies borrowed from biomechanics to guarantee the naturalness of reaching motion and expands the reachable space to enrich the flexibility of human behavior. We exploit the arm-reachable workspace to guide the motion sampling, and construct different low-dimensional space for each reaching strategy by PCA to reduce the search space, so as to make BLRC fast deal with huge mocap data set. Moreover, we also use the optimization method in these low-dimensional spaces to further speed up the convergence of motion synthesis with the help of the accurate starting point in data space during the search process. We demonstrate the power of the BLRC with more lifelike and complex reaching motion.