论文标题
迈向在长期互动中影响人类的机器人
Towards Robots that Influence Humans over Long-Term Interaction
论文作者
论文摘要
当人类与机器人互动时，不可避免地会影响。考虑一辆在人类附近行驶的自动驾驶汽车：自动驾驶汽车的速度和转向将影响人类驾驶方式。先前的作品开发了框架，使机器人能够影响人类对所需的行为。但是，尽管这些方法在短期（即前几个人类机器人相互作用）中有效，但我们在这里探索了长期影响（即同一人与机器人之间的反复相互作用）。我们的主要见解是，人类是动态的：人们适应机器人，一旦人类学会预见机器人的行为，现在影响力的行为可能会降低。有了这种见解，我们在实验上证明了一种普遍的游戏理论形式主义，用于产生有影响力的机器人行为，而不是重复互动的有效性降低。接下来，我们为Stackelberg游戏提出了三个修改，这些游戏使机器人的政策既有影响力又无法预测。我们最终在模拟和用户研究中测试了这些修改：我们的结果表明，故意使他们的行为更难预期的机器人能够更好地维持对长期互动的影响。在此处查看视频：https：//youtu.be/ydo83cgjz2q
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
