论文标题
让我们合作：基于遗憾的机器人操作的反应性综合
Let's Collaborate: Regret-based Reactive Synthesis for Robotic Manipulation
论文作者
论文摘要
随着机器人获得进入我们以人为中心的世界的能力，它们需要形式主义和算法，以实现智能有效的互动。这是具有挑战性的，特别是对于可能需要与人类协作的复杂任务的机器人操纵者。先前的工作通过反应性综合来解决此问题，并为机器人生成策略，以通过假设对抗性人来保证任务完成。尽管此假设给出了声音解决方案，但它导致了对人类意图不可知的“不友好”机器人。我们通过使用遗憾的概念来提出问题来放松这一假设。我们确定一个适当的定义，以备后遗憾，并开发最小化的综合框架，使机器人能够在可能的同时寻求合作，同时保留任务完成保证。我们通过各种案例研究说明了我们框架的功效。
As robots gain capabilities to enter our human-centric world, they require formalism and algorithms that enable smart and efficient interactions. This is challenging, especially for robotic manipulators with complex tasks that may require collaboration with humans. Prior works approach this problem through reactive synthesis and generate strategies for the robot that guarantee task completion by assuming an adversarial human. While this assumption gives a sound solution, it leads to an "unfriendly" robot that is agnostic to the human intentions. We relax this assumption by formulating the problem using the notion of regret. We identify an appropriate definition for regret and develop regret-minimizing synthesis framework that enables the robot to seek cooperation when possible while preserving task completion guarantees. We illustrate the efficacy of our framework via various case studies.
