Learning from Demonstration in the Wild

Abstract-- Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically relies on artificially generated demonstrations or specially deployed sensors and has not generally been able to leverage the copious demonstrations available in the wild: those that capture behaviour that was occurring anyway using sensors that were already deployed for another purpose, e.g., traffic camera footage capturing demonstrations of natural behaviour of vehicles, cyclists, and pedestrians. We propose video to behaviour (ViBe), a new approach to learning models of road user behaviour that requires as input only unlabelled raw video data of a traffic scene collected from a single, monocular, uncalibrated camera with ordinary resolution. Our approach calibrates the camera, detects relevant objects, tracks them through time, and uses the resulting trajectories to perform LfD, yielding models of naturalistic behaviour. We apply ViBe to raw videos of a traffic intersection and show that it can learn purely from videos, without additional expert knowledge. Learning from demonstration (LfD) is a machine learning technique that can learn complex behaviours from a dataset of expert trajectories, called demonstrations. LfD is particularly useful in settings where hand-coding behaviour, or engineering a suitable reward function, is too difficult or labour intensive. While LfD has succeeded in a wide range of problems [1], [2], [3], nearly all methods rely on either artificially generated demonstrations (e.g., from laboratory subjects) or those collected by specially deployed sensors (e.g., MOCAP). These restrictions greatly limit the practical applicability of LfD, which to date has largely not been able to leverage the copious demonstrations available in the wild: those that capture behaviour that was occurring anyway using sensors that were already deployed for other purposes. For example, consider the problem of training autonomous vehicles to navigate in the presence of human road users.