Socially CompliAnt Navigation Dataset (SCAND): A Large-Scale Dataset of Demonstrations for Social Navigation

TLDR

Social navigation enables autonomous agents to move safely among humans, and its importance has risen with the deployment of mobile robots in public and domestic settings, yet progress is limited by a shortage of large‑scale, real‑world demonstration data. This work introduces SCAND, a large‑scale first‑person‑view dataset of socially compliant navigation demonstrations, to facilitate imitation learning for socially aware robot navigation. SCAND comprises 8.7 h of 138 trajectories covering 25 mi of multi‑modal data (lidar, joystick, odometry, vision, inertial) collected on Boston Dynamics Spot and Clearpath Jackal by four human operators in indoor and outdoor environments, and is validated through real‑world robot experiments. Policies trained via imitation learning on SCAND were shown to exhibit socially compliant behaviors in real‑world robot trials.

Abstract

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 tele-operated 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.