Biomimetic grasp planning for cortical control of a robotic hand

In this paper we outline a grasp planning system designed to augment the cortical control of a prosthetic arm and hand. A key aspect of this system it the ability to combine online user input and autonomous planning to enable the execution of stable grasping tasks. While user input can ultimately be of any modality, the system is being designed to adapt to partial or noisy information obtained from grasp-related activity in the primate motor cortex. First, principal component analysis is applied to the observed kinematics of physiologic grasping to reduce the dimensionality of hand posture space and simplify the planning task for on-line use. The planner then accepts control input in this reduced-dimensionality space, and uses it as a seed for a hand posture optimization algorithm based on simulated annealing. We present two applications of this algorithm, using data collected from both primate and human subjects during grasping, to demonstrate its ability to synthesize stable grasps using partial control input in real or near-real time.