Nonlinear optimization for human-like movements of a high degree of freedom robotics arm-hand system

Abstract

The design of autonomous robots, able to closely cooperate with human users in shared tasks, provides many new challenges for robotics research. Compared to industrial applications, robots working in human environments will need to have human-like abilities in their cognitive and motor behaviors. Here we present a model for generating trajectories of a high degree of freedom robotics arm-hand system that reflects optimality principles of human motor control. The process of finding a human-like trajectory among all possible solutions is formalized as a large-scale nonlinear optimization problem. We compare numerically three existing solvers, IPOPT, KNITRO and SNOPT, in terms of their real-time performance in different reach-to-grasp problems that are part of a human-robot interaction task. The results show that the SQP methods obtain better results than the IP methods. SNOPT finds optimal solutions for all tested problems in competitive computational times, thus being the one that best serves our purpose.