Abstract-One of the key problems in planning and control of redundant robots is the fast generation of controls when multiple tasks and constraints need to be satisfied. In the literature, this problem is classically solved by multi-task prioritized approaches, where the priority of each task is determined by a weight function, describing the task strict/soft priority. In this paper, we propose to leverage machine learning techniques to learn the temporal profiles of the task priorities, represented as parametrized weight functions: we automatically determine their parameters through a stochastic optimization procedure. We show the effectiveness of the proposed method on a simulated 7 DOF Kuka LWR and both a simulated and a real Kinova Jaco arm. We compare the performance of our approach to a state-of-the-art method based on soft task prioritization, where the task weights are typically hand-tuned.

Multi-task prioritized controllers are able to generate complex robot behaviors that concurrently satisfy several tasks and constraints. To perform, they often require a human expert to define the evolution of the task priorities in time. In a previous paper [1] we proposed a framework to automatically learn the task priorities thanks to a stochastic optimization algorithm (CMA-ES) maximizing the robot performance on a certain behavior. Here, we learn the task priorities that maximize the robot performance, ensuring that the optimized priorities lead to safe behaviors that never violate any of the robot and problem constraints. We compare three constrained variants of CMA-ES on several benchmarks, among which two are new robotics benchmarks of our design using the KUKA LWR. We retain (1+1)-CMA-ES with covariance constrained adaptation [2] as the best candidate to solve our problems, and we show its effectiveness on two whole-body experiments with the iCub humanoid robot