Navigation strategies that intentionally incorporate contact with humans (i.e. "contact-based" social navigation) in crowded environments are largely unexplored even though collision-free social navigation is a well studied problem. Traditional social navigation frameworks require the robot to stop suddenly or "freeze" whenever a collision is imminent. This paradigm poses two problems: 1) freezing while navigating a crowd may cause people to trip and fall over the robot, resulting in more harm than the collision itself, and 2) in very dense social environments where collisions are unavoidable, such a control scheme would render the robot unable to move and preclude the opportunity to study how humans incorporate robots into these environments. However, if robots are to be meaningfully included in crowded social spaces, such as busy streets, subways, stores, or other densely populated locales, there may not exist trajectories that can guarantee zero collisions. Thus, adoption of robots in these environments requires the development of minimally disruptive navigation plans that can safely plan for and respond to contacts. We propose a learning-based motion planner and control scheme to navigate dense social environments using safe contacts for an omnidirectional mobile robot. The planner is evaluated in simulation over 360 trials with crowd densities varying between 0.0 and 1.6 people per square meter. Our navigation scheme is able to use contact to safely navigate in crowds of higher density than has been previously reported, to our knowledge.

翻译：在拥挤环境中，有意将与人接触（即“接触式”社交导航）纳入考量的导航策略在很大程度上尚未被探索，尽管无碰撞社交导航是一个被广泛研究的问题。传统社交导航框架要求机器人在即将发生碰撞时突然停止或“冻结”。这种范式带来了两个问题：1) 在人群中导航时突然停止可能导致人们被机器人绊倒并受伤，其危害甚至比碰撞本身更大；2) 在碰撞不可避免的极高密度社交环境中，此类控制方案将使机器人无法移动，并阻碍研究人类如何将机器人融入这些环境的机会。然而，如果要在拥挤的社交空间（如繁忙的街道、地铁、商店或其他人口密集区域）中有意义地纳入机器人，可能并不存在能够保证零碰撞的轨迹。因此，在这些环境中应用机器人需要开发最小干扰的导航方案，该方案能够安全地为接触事件进行规划并做出响应。我们提出了一种基于学习的运动规划器与控制方案，利用安全接触使全向移动机器人在密集社交环境中导航。该规划器在仿真中进行了360次试验评估，人群密度在每平方米0.0至1.6人之间变化。据我们所知，我们的导航方案能够利用接触在高于先前报道的人群密度下实现安全导航。