  A dynamic autonomy allocation framework automatically shifts how much control
lies with the human versus the robotics autonomy, for example based on factors
such as environmental safety or user preference. To investigate the question of
which factors should drive dynamic autonomy allocation, we perform a human
subject study to collect ground truth data that shifts between levels of
autonomy during shared-control robot operation. Information streams from the
human, the interaction between the human and the robot, and the environment are
analyzed. Machine learning methods -- both classical and deep learning -- are
trained on this data. An analysis of information streams from the human-robot
team suggests features which capture the interaction between the human and the
robotics autonomy are the most informative in predicting when to shift autonomy
levels. Even the addition of data from the environment does little to improve
upon this predictive power. The features learned by deep networks, in
comparison to the hand-engineered features, prove variable in their ability to
represent shift-relevant information. This work demonstrates the classification
power of human-only and human-robot interaction information streams for use in
the design of shared-control frameworks, and provides insights into the
comparative utility of various data streams and methods to extract
shift-relevant information from those data.
