  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
