  A major challenge in research involving artificial intelligence (AI) is the
development of algorithms that can find solutions to problems that can
generalize to different environments and tasks. Unlike AI, humans are adept at
finding solutions that can transfer. We hypothesize this is because their
solutions are informed by causal models. We propose to use human-guided causal
knowledge to help robots find solutions that can generalize to a new
environment. We develop and test the feasibility of a language interface that
na\"ive participants can use to communicate these causal models to a planner.
We find preliminary evidence that participants are able to use our interface
and generate causal models that achieve near-generalization. We outline an
experiment aimed at testing far-generalization using our interface and describe
our longer terms goals for these causal models.
