Inter-finger coordination and postural synergies in robot hands via mechanical implementation of principal components analysis

Human hands employ characteristic patterns of actuation, or synergies, that contain much of the information required to describe an entire hand shape. In some cases, 80% or more of the total information can be described with only two scalar component values. Robotic hands, however, commonly only couple intra-finger joints, and rarely take advantage of this inter-finger coordination. In this paper, real-world data on a variety of human hand postures was collected using a data glove, and principal components analysis was used to calculate these synergies, resulting in what we call eigenpostures. A novel mechanism design is presented to combine the eigenpostures and drive a 17-degree-of-freedom 5-fingered robot hand. The hand uses only 2 DC motors to accurately recreate a wide range of hand shapes. We also present a design improvement that allows us to distinguish between high-precision and low-precision tasks, as well as greatly reduce overall error.