  In this work, we point out the problem of observed adversaries for deep
policies. Specifically, recent work has shown that deep reinforcement learning
is susceptible to adversarial attacks where an observed adversary acts under
environmental constraints to invoke natural but adversarial observations. This
setting is particularly relevant for HRI since HRI-related robots are expected
to perform their tasks around and with other agents. In this work, we
demonstrate that this effect persists even with low-dimensional observations.
We further show that these adversarial attacks transfer across victims, which
potentially allows malicious attackers to train an adversary without access to
the target victim.
