论文标题
减轻驾驶员和半自动化车之间产生的不良紧急行为
Mitigating undesirable emergent behavior arising between driver and semi-automated vehicle
论文作者
论文摘要
基于对个体代理的理解，不能完全预测在联合人类机器人系统中产生的新兴行为。通常，机器人行为受算法的控制，该算法优化了应定量捕获联合系统目标的奖励功能。尽管可以更新奖励功能以更好地满足人类需求，但不能保证不会发生复杂且可变的人类需求。算法在与人类和本质上不可预测的人居住世界互动时可能会学习不良行为，从而与人类用户或旁观者产生进一步的失误。结果，人类的行为可能与预期不同，从而导致机器人以不同的方式学习和不希望的行为出现。在这篇简短的论文中，我们指出，为减轻这种不受欢迎的紧急行为而设计人机相互作用，我们需要将人类机器人相互作用算法的进步与人类因素知识和专业知识相辅相成。更具体地说，我们主张一种三管齐下的方法，我们使用一个特别具有挑战性的人类互动示例来说明它：与半自动化车辆相互作用的驾驶员。应通过1）组合来缓解不良的紧急行为，包括车辆算法中的驾驶员行为机制和奖励功能，2）基于模型的方法，这些方法解释了互动引起的驾驶员行为适应和3）以驾驶员为中心的交互作用设计，可促进与每种代理人的近距离交流和近距离交流的驾驶员互动，并促进跨代理人的互动和惯用的互动。我们提供了我们小组最近的经验工作的例子，希望这对讨论新兴的人类机器人互动是有益的。
Emergent behavior arising in a joint human-robot system cannot be fully predicted based on an understanding of the individual agents. Typically, robot behavior is governed by algorithms that optimize a reward function that should quantitatively capture the joint system's goal. Although reward functions can be updated to better match human needs, this is no guarantee that no misalignment with the complex and variable human needs will occur. Algorithms may learn undesirable behavior when interacting with the human and the intrinsically unpredictable human-inhabited world, thereby producing further misalignment with human users or bystanders. As a result, humans might behave differently than anticipated, causing robots to learn differently and undesirable behavior to emerge. With this short paper, we state that to design for Human-Robot Interaction that mitigates such undesirable emergent behavior, we need to complement advancements in human-robot interaction algorithms with human factors knowledge and expertise. More specifically, we advocate a three-pronged approach that we illustrate using a particularly challenging example of safety-critical human-robot interaction: a driver interacting with a semi-automated vehicle. Undesirable emergent behavior should be mitigated by a combination of 1) including driver behavioral mechanisms in the vehicle's algorithms and reward functions, 2) model-based approaches that account for interaction-induced driver behavioral adaptations and 3) driver-centered interaction design that promotes driver engagement with the semi-automated vehicle, and the transparent communication of each agent's actions that allows mutual support and adaptation. We provide examples from recent empirical work in our group, in the hope this proves to be fruitful for discussing emergent human-robot interaction.
