Vision-Based Online Learning Kinematic Control for Soft Robots Using Local Gaussian Process Regression

Abstract

Soft robots, owing to their elastomeric material, ensure safe interaction with their surroundings. These robot compliance properties inevitably impose a tradeoff against precise motion control, as to which conventional model-based methods were proposed to approximate the robot kinematics. However, too many parameters, regarding robot deformation and external disturbance, are difficult to obtain, even if possible, which could be very nonlinear. Sensors self-contained in the robot are required to compensate modeling uncertainties and external disturbances. Camera (eye) integrated at the robot end-effector (hand) is a common setting. To this end, we propose an eye-in-hand visual servo that incorporates with learning-based controller to accomplish more precise robotic tasks. Local Gaussian process regression is used to initialize and refine the inverse mappings online, without prior knowledge of robot and camera parameters. Experimental validation is also conducted to demonstrate the hyperelastic robot can compensate an external variable loading during trajectory tracking.