  As more robots act in physical proximity to people, it is essential to ensure
they make decisions and execute actions that align with human values. To do so,
robots need to understand the true intentions behind human-issued commands. In
this paper, we define a safe robot as one that receives a natural-language
command from humans, considers an action in response to that command, and
accurately predicts how humans will judge that action if is executed in
reality. Our contribution is two-fold: First, we introduce a web platform for
human users to propose commands to simulated robots. The robots receive
commands and act based on those proposed commands, and then the users provide
positive and/or negative reinforcement. Next, we train a critic for each robot
to 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 grounds
language: The critics show that two of the robots used in the experiment
achieve a lower prediction error than the others. Thus, those two robots are
safer, according to our definition, since they ground the proposed command more
accurately.
