Learning soft task priorities for safe control of humanoid robots with constrained stochastic optimization

International audienceMulti-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 using a stochastic optimization algorithm (CMA-ES), maximizing the robot performance for 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

Redundancy resolution for the human-arm-like manipulator

The human-arm-like manipulator has a seven-degree-of-freedom kinematic structure obtained by adding a roll joint to the shoulder of the PUMA geometry. This mechanism has higher dexterity than conventional six-degree-of-freedom arms, and allows the elimination of the internal singularities at the shoulder and the wrist. The contribution of the present work is to exploit redundancy to accomplish singularity avoidance and possibly recover the original PUMA design. The inverse for kinematics problem is solved at the velocity level by means of a closed-loop algorithm in the framework of task space augmentation with task priority. Three case studies demonstrate the effectiveness of the proposed technique