社会兼容导航数据集:社会导航示范集大型数据集 (Socially Compliant Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation)

Social navigation is the capability of an autonomous agent, such as a robot, to navigate in a 'socially compliant' manner in the presence of other intelligent agents such as humans. With the emergence of autonomously navigating mobile robots in human populated environments (e.g., domestic service robots in homes and restaurants and food delivery robots on public sidewalks), incorporating socially compliant navigation behaviors on these robots becomes critical to ensuring safe and comfortable human robot coexistence. To address this challenge, imitation learning is a promising framework, since it is easier for humans to demonstrate the task of social navigation rather than to formulate reward functions that accurately capture the complex multi objective setting of social navigation. The use of imitation learning and inverse reinforcement learning to social navigation for mobile robots, however, is currently hindered by a lack of large scale datasets that capture socially compliant robot navigation demonstrations in the wild. To fill this gap, we introduce Socially CompliAnt Navigation Dataset (SCAND) a large scale, first person view dataset of socially compliant navigation demonstrations. Our dataset contains 8.7 hours, 138 trajectories, 25 miles of socially compliant, human teleoperated driving demonstrations that comprises multi modal data streams including 3D lidar, joystick commands, odometry, visual and inertial information, collected on two morphologically different mobile robots a Boston Dynamics Spot and a Clearpath Jackal by four different human demonstrators in both indoor and outdoor environments. We additionally perform preliminary analysis and validation through real world robot experiments and show that navigation policies learned by imitation learning on SCAND generate socially compliant behaviors

翻译：社会导航是一个自主代理人(如机器人)在人类等其他智能代理人在场的情况下以“符合社会标准”的方式航行的能力。随着在人类居住环境中(如家中和餐馆的家用服务机器人和在公共人行道的送食品机器人)自动导航移动机器人的出现(例如家用和餐馆的家用服务机器人和在公共人行道的送货机器人),在这些机器人上纳入符合社会标准的导航行为对于确保安全和舒适的人类机器人共存至关重要。为了应对这一挑战,模仿学习是一个充满希望的框架,因为人类更容易展示社会导航的任务,而不是制定准确捕捉复杂的多目标社会导航设置的奖励功能。然而,目前由于缺乏大规模的数据集,无法捕捉符合社会要求的机器人,因此难以在这些机器人上纳入符合社会要求的机器人导航行为。为了填补这一空白,我们引入了社会兼容安特导航数据集(SCAND),一个大尺度,第一个人查看符合社会合规的导航演示数据集。我们的数据集有8.7小时,138个内部跟踪,25英里的社交导航学习和反向社会导航学习学习学习的学习学习, 人类运动的轨道上流流 4个流流数据演示,通过收集、流流路路路的流数据演示,通过双流学流学、流学、流、流、流学、流学、流学、流路路路路路路路路路的智能学习、流、流、流、流学、流、流、流、流、流、流学、流学、流学、流、流、流学、流学、流学、流学、流、流学、流路路路路路路路路路路演演演演演演演、流学、流学、流学、流学、流学、流学、流学、流学、流、流学、流学、流学、流学、流学、流、流、流、流学、流、流、流、流、流、流学、流、流、流学、流、流学、流学、流学、流、流、流、流、流学、流、流、流、流、路、流、流、流学、路、流学、流、流学、路路路路路路路路路路路路路路路路路路演演、路路路