AppGAN: Generative Adversarial Networks for Generating Robot Approach Behaviors into Small Groups of People

Abstract

Robots that navigate to approach free-standing conversational groups should do so in a safe and socially acceptable manner. This is challenging since it not only requires the robot to plot trajectories that avoid collisions with members of the group, but also to do so without making those in the group feel uncomfortable, for example, by moving too close to them or approaching them from behind. Previous trajectory prediction models focus primarily on formations of walking pedestrians, and those models that do consider approach behaviours into free-standing conversational groups typically have handcrafted features and are only evaluated via simulation methods, limiting their effectiveness. In this paper, we propose AppGAN, a novel trajectory prediction model capable of generating trajectories into free-standing conversational groups trained on a dataset of safe and socially acceptable paths. We evaluate the performance of our model with state-of-the-art trajectory prediction methods on a semi-synthetic dataset. We show that our model outperforms baselines by taking advantage of the GAN framework and our novel group interaction module.