The Predictive Kinematic Control Tree: Enhancing Teleoperation of Redundant Robots through Probabilistic User Models

Abstract

When teleoperating complex robotic manipula-tors, operators often find it most natural to issue commands that dictate end effector movements in task space. If the robot has redundant degrees of freedom, the translation of this com-mand from task space into configuration space can affect the robot's maneuverability, smoothness of motion, and the general precision of the teleoperated system. In this paper, we propose a novel method for performing this translation that predicts future operator commands in order to choose joint motions that maintain maneuverability in future timesteps. We introduce a Predictive Kinematic Control Tree (PrediKCT) that optimizes joint movement in the nullspace of the Jacobian over multiple future timesteps by reasoning over probabilistic models of the human operator. In essence, PrediKCT builds out and evaluates a tree of possible future commands. We implement this system on two simulated and one physical 7 -degree-of-freedom robotic arms and characterize performance by analyzing robot motions produced through multiple command trajectories with differing user model accuracies and tree parameters, demonstrating benefits to path accuracy over both a minimum-norm joint velocity solution and local optimization of joint movement.