4 accepted papers
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 perf…
Shared control for human-robot teams - where both the human and the robot's autonomy provide commands to the hardware - offers advantages over fully teleoperated or fully autonomous systems by utilizing the unique skill sets of both the human and robot's autonomy simultaneously. However, the mechani…
Humans and robots team together to perform tasks in various domains. Some tasks are easier to perform than others, but little work focuses on discovering the underlying mechanisms that affect perceived difficulty and task performance. To fill this gap, we propose a formalized approach to task charac…
Machine learning offers a fantastically powerful toolkit for building useful complexprediction systems quickly. This paper argues it is dangerous to think ofthese quick wins as coming for free. Using the software engineering frameworkof technical debt, we find it is common to incur massive ongoing m…