Aligning Robot Behaviors with Human Intents by Exposing Learned Behaviors and Resolving Misspecifications

Human-robot interaction is limited in large part by the challenge of writing correct specifications for robots. The research community wants alignment between humans' goals and robot behaviors, but this alignment is very hard to achieve. My research tackles this problem. I view alignment as the consequence of iterative design and ample testing, and I design methods in service of these processes. I first study how humans currently write reward functions, and I profile some of the typical errors they make when doing so. I then study how humans can inspect the behaviors robots learn from any given specification. A typical approach to this mandates unstructured or hand-designed test cases; I instead introduce a Bayesian inference method for finding behavior examples which cover information-rich test cases. Alongside finding these behavior examples, I study how these examples should be presented to the human through applying cognitive theories of human concept learning. For the remainder of my thesis, I am pursuing two open questions. My first question concerns how these components can be combined such that humans are able to iteratively design better behavioral specifications. My second question concerns how robots can better interpret humans' erroneous specifications and attempt to infer their true intent, in spite of the errors.