Evolution Strategies Learning With Variable Impedance Control for Grasping Under Uncertainty

During a robot's interaction with the environment, it is necessary to ensure the safety and robustness of the robot's movements. To improve the safety and adaptiveness of robots in performing complex movement tasks, a novel method called covariance matrix adaptation-evolution strategies (CMA-ES) for learning complex and high-dimensional motor skills is presented. Considering the complex motion model of trajectories, dynamic movement primitives (DMPs), which is a generic method for trajectories modeling in attractor landscape based on differential dynamic systems, is used to represent the robot's trajectories. CMA-ES offers a theoretical rule for updating the parameters of DMPs and a variable impedance controller, which can reduce the impact of noisy environment on the robot's movement. In this paper, we propose two hierarchies for controlling the robot: the high-level neural-dynamic network optimization for redundancy resolution in task space and the low-level CMA-ES fusing with DMPs for learning trajectories in joint space. In this paper, CMA-ES method is explored to learn variable impedance control and the performance of the proposed method in learning the robot's movements is also tested.