论文标题
鼓励人类与机器人团队的互动：清晰且公平的子任务分配
Encouraging Human Interaction with Robot Teams: Legible and Fair Subtask Allocations
论文作者
论文摘要
最近的作品探讨了人类与机器人团队之间的合作。如果人类已经与机器人团队合作，这些方法是有道理的。但是，机器人应该如何鼓励附近的人类加入他们的团队呢？受行为经济学的启发，我们认识到人类不仅关心团队效率 - 人类对团队动态也有偏见和期望。我们的假设是，包容性机器人将任务划分的方式（即机器人如何将较大的任务分为子任务分配）应该对人类合作伙伴来说既可以清晰又公平。在本文中，我们介绍了一种双重优化方法，该方法使机器人团队能够识别高级子任务分配和低级轨迹，以优化可透明度，公平性或两个目标的组合。然后，我们在人类观看或与机器人团队一起玩的研究中测试了所得算法。我们发现，我们生成清晰的团队的方法使人类的角色变得清晰，并且人类通常更喜欢与清晰的团队一起加入和合作，而不是仅优化效率的团队。结合公平性与易读性进一步鼓励参与：当人类使用机器人玩耍时，我们发现他们更喜欢（潜在效率低下）子任务或努力均匀分配的团队。在此处查看我们的研究视频https://youtu.be/cfn7o5na3mg
Recent works explore collaboration between humans and teams of robots. These approaches make sense if the human is already working with the robot team; but how should robots encourage nearby humans to join their teams in the first place? Inspired by behavioral economics, we recognize that humans care about more than just team efficiency -- humans also have biases and expectations for team dynamics. Our hypothesis is that the way inclusive robots divide the task (i.e., how the robots split a larger task into subtask allocations) should be both legible and fair to the human partner. In this paper we introduce a bilevel optimization approach that enables robot teams to identify high-level subtask allocations and low-level trajectories that optimize for legibility, fairness, or a combination of both objectives. We then test our resulting algorithm across studies where humans watch or play with robot teams. We find that our approach to generating legible teams makes the human's role clear, and that humans typically prefer to join and collaborate with legible teams instead of teams that only optimize for efficiency. Incorporating fairness alongside legibility further encourages participation: when humans play with robots, we find that they prefer (potentially inefficient) teams where the subtasks or effort are evenly divided. See videos of our studies here https://youtu.be/cfN7O5na3mg
