  When humans interact with robots influence is inevitable. Consider an
autonomous car driving near a human: the speed and steering of the autonomous
car will affect how the human drives. Prior works have developed frameworks
that enable robots to influence humans towards desired behaviors. But while
these approaches are effective in the short-term (i.e., the first few
human-robot interactions), here we explore long-term influence (i.e., repeated
interactions between the same human and robot). Our central insight is that
humans are dynamic: people adapt to robots, and behaviors which are influential
now may fall short once the human learns to anticipate the robot's actions.
With this insight, we experimentally demonstrate that a prevalent
game-theoretic formalism for generating influential robot behaviors becomes
less effective over repeated interactions. Next, we propose three modifications
to Stackelberg games that make the robot's policy both influential and
unpredictable. We finally test these modifications across simulations and user
studies: our results suggest that robots which purposely make their actions
harder to anticipate are better able to maintain influence over long-term
interaction. See videos here: https://youtu.be/ydO83cgjZ2Q
