2 research outputs found
Should Collaborative Robots be Transparent?
We often assume that robots which collaborate with humans should behave in
ways that are transparent (e.g., legible, explainable). These transparent
robots intentionally choose actions that convey their internal state to nearby
humans: for instance, a transparent robot might exaggerate its trajectory to
indicate its goal. But while transparent behavior seems beneficial for
human-robot interaction, is it actually optimal? In this paper we consider
collaborative settings where the human and robot have the same objective, and
the human is uncertain about the robot's type (i.e., the robot's internal
state). We extend a recursive combination of Bayesian Nash equilibrium and the
Bellman equation to solve for optimal robot policies. Interestingly, we
discover that it is not always optimal for collaborative robots to be
transparent; instead, human and robot teams can sometimes achieve higher
rewards when the robot is opaque. In contrast to transparent robots, opaque
robots select actions that withhold information from the human. Our analysis
suggests that opaque behavior becomes optimal when either (a) human-robot
interactions have a short time horizon or (b) users are slow to learn from the
robot's actions. We extend this theoretical analysis to user studies across 43
total participants in both online and in-person settings. We find that --
during short interactions -- users reach higher rewards when working with
opaque partners, and subjectively rate opaque robots as about equal to
transparent robots. See videos of our experiments here:
https://youtu.be/u8q1Z7WHUu
Towards Robots that Influence Humans over Long-Term Interaction
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/ydO83cgjZ2