We are considering the problem of controlling a team of robotic bodyguards protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders. This task is part of a much larger class of problems involving coordinated robot behavior in the presence of humans. This problem is challenging due to the large number of active entities with different agendas, the need of cooperation between the robots as well as the requirement to take into consideration criteria such as social norms and unobtrusiveness in addition to the main goal of VIP safety. Furthermore, different settings such as street, public space or red carpet require very different behavior from the robot. We describe how a multi-agent reinforcement learning approach can evolve behavior policies for teams of robot bodyguards that compare well with hand-engineered approaches. Furthermore, we show that an algorithm inspired by universal value function approximators can learn policies that exhibit appropriate, distinct behavior in environments with different requirements.

中文翻译：

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机器人保镖团队情景适当协作行为的出现

我们正在考虑在中立和/或敌对旁观者在场的情况下控制一组机器人保镖保护 VIP 免受人身攻击的问题。这项任务是涉及在人类面前协调机器人行为的更大类别问题的一部分。由于存在大量具有不同议程的活动实体，机器人之间需要合作，以及除了 VIP 安全的主要目标之外，还需要考虑社会规范和不引人注目等标准，因此这个问题具有挑战性。此外，不同的环境，如街道、公共空间或红地毯，需要机器人的行为非常不同。我们描述了多智能体强化学习方法如何为机器人保镖团队制定与手工设计方法相媲美的行为策略。此外，我们表明，受通用值函数逼近器启发的算法可以学习在具有不同要求的环境中表现出适当、不同行为的策略。