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Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for thedoi:10.18653/v1/w17-2809 dblp:conf/acl/KaramchetiWARGW17 fatcat:aqv5k2cd6nazxbce6r5ig46wdq