In-hand manipulation with synergistic actuated robotic hands: an MPC-based approach

In-hand manipulation, or dexterous manipulation, is one of the most complex challenges in robotics as it requires the accurate coordination of multiple degrees of freedom. While several solutions have been presented for fully actuated hands, less work has focused on underactuated grippers. Synergies can be interpreted as a method for coupling joint motions, constraining the hand's degrees of freedom, and thereby reducing the number of control inputs. In this paper, we propose a model predictive control scheme (MPC) that integrates synergies to implement dexterous in-hand manipulation with robotic hands. In the MPC formulation, synergies can either be considered as constraints on the joint variables or are directly inserted in the system function with a reallocation of the input variables acting on the joints. Through several sets of simulations we compare these two approaches and show their main features.