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As more robots act in physical proximity to people, it is essential to ensurethey make decisions and execute actions that align with human values. To do so,robots need to understand the true intentions behind human-issued commands. Inthis paper, we define a safe robot as one that receives a natural-languagecommand from humans, considers an action in response to that command, andaccurately predicts how humans will judge that action if is executed inreality. Our contribution is two-fold: First, we introduce a web platform forhuman users to propose commands to simulated robots. The robots receivecommands and act based on those proposed commands, and then the users providepositive and/or negative reinforcement. Next, we train a critic for each robotto predict the crowd's responses to one of the crowd-proposed commands. Second,we show that the morphology of a robot plays a role in the way it groundslanguage: The critics show that two of the robots used in the experimentachieve a lower prediction error than the others. Thus, those two robots aresafer, according to our definition, since they ground the proposed command moreaccurately.

Morphology dictates a robot's ability to ground crowd-proposed language