  As robots are increasingly deployed in real-world scenarios, a key question
is how to best transfer knowledge learned in one environment to another, where
shifting constraints and human preferences render adaptation challenging. A
central challenge remains that often, it is difficult (perhaps even impossible)
to capture the full complexity of the deployment environment, and therefore the
desired tasks, at training time. Consequently, the representation, or
abstraction, of the tasks the human hopes for the robot to perform in one
environment may be misaligned with the representation of the tasks that the
robot has learned in another. We postulate that because humans will be the
ultimate evaluator of system success in the world, they are best suited to
communicating the aspects of the tasks that matter to the robot. Our key
insight is that effective learning from human input requires first explicitly
learning good intermediate representations and then using those representations
for solving downstream tasks. We highlight three areas where we can use this
approach to build interactive systems and offer future directions of work to
better create advanced collaborative robots.
