Real-time tuning soft task priorities with quadratic programming.
Abstract— When robot simultaneously executes multiple tasks with potential incompatibilities, in order to realize objectives and satisfy constraints, how to assign appropriate priorities to each task is an open research problem. Most of the existing methods are only aimed at tuning task weights in static scenario. In this paper, for the scenario with randomly dynamic factors, we propose a real-time task priorities tuning method based on global objectives and constraints to deal with conflicting tasks for adapting environmental changes. The soft task priorities are real-time computed by a constrained optimization problem at each time point. For fast computation and smooth transition to task weights, we transform the problem to the formation of quadratic programming. We benchmark our method on a simulated 6 DOF UR5 arm comparing with realtime Bayesian Optimization tuning and no-tuning. And we also validate the effectiveness of our method in experiments with randomly dynamic obstacle or target.