Look Ahead Optimization for Managing Nullspace in Cartesian Impedance Control of Dual-Arm Robots

Abstract

This paper presents a method for handling nullspace challenges in Cartesian impedance control of a dual-arm KUKA IIWA robot by employing a Look ahead Controller(LAC) in nullspace. Ambidexterity is crucial for dual-arm robots to perform complex tasks that require coordinated use of both arms. Cartesian impedance control provides significant advantages in dual-arm manipulation tasks, especially in imitation learning for reproducing learned compliant interactions and precise control of end-effector poses. This approach enables the learned tasks to be robot-agnostic, facilitating transfer to other robotic systems. However, the nullspace handling of cartesian impedance control is very challenging. In this paper, we address this issue to handle kinematic constraints and facilitate avoiding singularities, joint limits, and collision in nullspace or redundant space of dual arms with each other with the help of a LAC. The proposed approach utilizes Sequential QP in the optimization loop of LAC for estimating optimal joint configurations for a horizon in redundant space, this provides the safe and efficient operation. Results are provided in this paper for two trajectories and compared with and without optimization, results demonstrate the method’s effectiveness in maintaining desired end-effector poses while avoiding kinematic constraints and nullspace collisions.