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.