When studying robots collaborating with humans, much of the focus has been on robot
policies that coordinate fluently with human teammates in collaborative tasks. However, less …

We present a method for learning human-robot collaboration policy from human-human
collaboration demonstrations. An effective robot assistant must learn to handle diverse …

Decisions are increasingly taken by both humans and machine learning models. However,
machine learning models are currently trained for full automation—they are not aware that …

Most supervised learning models are trained for full automation. However, their predictions
are sometimes worse than those by human experts on some specific instances. Motivated by …

Abstract Offline Learning from Demonstrations (OLfD) is valuable in domains where trial-and-
error learning is infeasible or specifying a cost function is difficult, such as robotic surgery …

With the adoption of autonomous vehicles on our roads, we will witness a mixed-autonomy
environment where autonomous and human-driven vehicles must learn to co-exist by …

Abstract Learning from Demonstration (LfD) approaches empower end-users to teach robots
novel tasks via demonstrations of the desired behaviors, democratizing access to robotics …

Much recent work has focused on how an agent can learn what to do from human feedback,
leading to two major paradigms. The first paradigm is reward learning, in which the agent …

While advances in multi-agent learning have enabled the training of increasingly complex
agents, most existing techniques produce a final policy that is not designed to adapt to a …

There is a growing interest in designing robots that can work alongside humans. Such
robots will undoubtedly be expected to explain their behavior and decisions. While …