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A major challenge in research involving artificial intelligence (AI) is thedevelopment of algorithms that can find solutions to problems that cangeneralize to different environments and tasks. Unlike AI, humans are adept atfinding solutions that can transfer. We hypothesize this is because theirsolutions are informed by causal models. We propose to use human-guided causalknowledge to help robots find solutions that can generalize to a newenvironment. We develop and test the feasibility of a language interface thatna\"ive participants can use to communicate these causal models to a planner.We find preliminary evidence that participants are able to use our interfaceand generate causal models that achieve near-generalization. We outline anexperiment aimed at testing far-generalization using our interface and describeour longer terms goals for these causal models.

Using Human-Guided Causal Knowledge for More Generalized Robot Task Planning