The field of wearable robotics has seen major advances in recent years, largely owing to an
intense focus on optimizing device behavior to accomplish a narrow set of objectives. This …

User preference is a promising objective for the control of robotic exoskeletons because it
may capture the multifactorial nature of exoskeleton use. However, to use it, we must first …

One challenge to achieving widespread success of augmentative exoskeletons is accurately
adjusting the controller to provide cooperative assistance with their wearer. Often, the …

Learning from human feedback has shown to be a useful approach in acquiring robot
reward functions. However, expert feedback is often assumed to be drawn from an …

Reward functions are a common way to specify the objective of a robot. As designing reward
functions can be extremely challenging, a more promising approach is to directly learn …

Bringing dynamic robots into the wild requires a tenuous balance between performance and
safety. Yet controllers designed to provide robust safety guarantees often result in …

Humans have internal models of robots (like their physical capabilities), the world (like what
will happen next), and their tasks (like a preferred goal). However, human internal models …

Reward learning is a fundamental problem in human-robot interaction to have robots that
operate in alignment with what their human user wants. Many preference-based learning …

Today's robots are increasingly interacting with people and need to efficiently learn
inexperienced user's preferences. A common framework is to iteratively query the user about …

Robot policies need to adapt to human preferences and/or new environments. Human
experts may have the domain knowledge required to help robots achieve this adaptation …