Modulating Human Input for Shared Autonomy in Dynamic Environments

Abstract

Many robotic tasks require human interaction through teleoperation to achieve high performance. However, in industrial applications these methods often require high levels of concentration and manual dexterity leading to high cognitive loads and dangerous working conditions. Shared autonomy attempts to address these issues by blending human and autonomous reasoning, relieving the burden of precise motor control, tracking, and localization. In this paper we propose an optimization-based representation for shared autonomy in dynamic environments. We ensure real-time tractability by modulating the human input with the information of the changing environment in the same task space, instead of adding it to the optimization cost or constraints. We illustrate the method with two real world applications: grasping objects in a cluttered environment, and a spraying task requiring sprayed linings with greater homogeneity. Finally we use a 7 degree of freedom KUKA LWR arm to simulate the grasping and spraying experiments.