Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. However, a formal definition of compliance is not straightforward. On the other hand, empowerment has been used by artificial agents to learn complicated and generalized actions and also has been shown to be a good model for biological behaviors. In this paper, we go beyond the approach of classical \acf{RL} and provide our agent with intrinsic motivation using empowerment. In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion. In our experiments, we show that our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal. An interactive user-study shows that our method is considered more social than other state-of-the-art approaches by the participants.

中文翻译：

---

具有人类赋权驱动的深度强化学习的社交导航

移动机器人导航在过去几十年中得到了广泛的研究。与机器人和人类共享工作空间的协作方面在未来将变得越来越重要。因此，下一代移动机器人需要符合社会规范才能被人类合作者接受。然而，合规的正式定义并不简单。另一方面，授权已被人工代理用于学习复杂和广义的动作，并且已被证明是生物行为的良好模型。在本文中，我们超越了经典 \acf{RL} 的方法，并使用授权为我们的代理提供内在动机。与自我授权相反，采用我们方法的机器人会努力增强其环境中的人们的权能，因此他们不会受到机器人的干扰” 的存在和运动。在我们的实验中，我们表明我们的方法对人类有积极的影响，因为它最大限度地减少了与人类的距离，从而减少了人类旅行时间，同时有效地朝着自己的目标前进。一项交互式用户研究表明，参与者认为我们的方法比其他最先进的方法更具社交性。