Title: Learning Models for Shared Control of Human-Machine Systems with Unknown Dynamics,
Abstract: We present a novel approach to shared control of human-machine systems. Our
method assumes no a priori knowledge of the system dynamics. Instead, we learn
both the dynamics and information about the user's interaction from observation
through the use of the Koopman operator. Using the learned model, we define an
optimization problem to compute the optimal policy for a given task, and
compare the user input to the optimal input. We demonstrate the efficacy of our
approach with a user study. We also analyze the individual nature of the
learned models by comparing the effectiveness of our approach when the
demonstration data comes from a user's own interactions, from the interactions
of a group of users and from a domain expert. Positive results include
statistically significant improvements on task metrics when comparing a
user-only control paradigm with our shared control paradigm. Surprising results
include findings that suggest that individualizing the model based on a user's
own data does not effect the ability to learn a useful dynamic system. We
explore this tension as it relates to developing human-in-the-loop systems
further in the discussion.