  We present a shared control paradigm that improves a user's ability to
operate complex, dynamic systems in potentially dangerous environments without
a priori knowledge of the user's objective. In this paradigm, the role of the
autonomous partner is to improve the general safety of the system without
constraining the user's ability to achieve unspecified behaviors. Our approach
relies on a data-driven, model-based representation of the joint human-machine
system to evaluate, in parallel, a significant number of potential inputs that
the user may wish to provide. These samples are used to (1) predict the safety
of the system over a receding horizon, and (2) minimize the influence of the
autonomous partner. The resulting shared control algorithm maximizes the
authority allocated to the human partner to improve their sense of agency,
while improving safety. We evaluate the efficacy of our shared control
algorithm with a human subjects study (n=20) conducted in two simulated
environments: a balance bot and a race car. During the experiment, users are
free to operate each system however they would like (i.e., there is no
specified task) and are only asked to try to avoid unsafe regions of the state
space. Using modern computational resources (i.e., GPUs) our approach is able
to consider more than 10,000 potential trajectories at each time step in a
control loop running at 100Hz for the balance bot and 60Hz for the race car.
The results of the study show that our shared control paradigm improves system
safety without knowledge of the user's goal, while maintaining high-levels of
user satisfaction and low-levels of frustration. Our code is available online
at https://github.com/asbroad/mpmi_shared_control.
