We focus on robot navigation in crowded environments. To navigate safely and efficiently within crowds, robots need models for crowd motion prediction. Building such models is hard due to the high dimensionality of multiagent domains and the challenge of collecting or simulating interaction-rich crowd-robot demonstrations. While there has been important progress on models for offline pedestrian motion forecasting, transferring their performance on real robots is nontrivial due to close interaction settings and novelty effects on users. In this paper, we investigate the utility of a recent state-of-the-art motion prediction model (S-GAN) for crowd navigation tasks. We incorporate this model into a model predictive controller (MPC) and deploy it on a self-balancing robot which we subject to a diverse range of crowd behaviors in the lab. We demonstrate that while S-GAN motion prediction accuracy transfers to the real world, its value is not reflected on navigation performance, measured with respect to safety and efficiency; in fact, the MPC performs indistinguishably even when using a simple constant-velocity prediction model, suggesting that substantial model improvements might be needed to yield significant gains for crowd navigation tasks. Footage from our experiments can be found at https://youtu.be/mzFiXg8KsZ0.

翻译：我们关注拥挤环境中的机器人导航问题。为了在人群中安全高效地导航，机器人需要群体运动预测模型。由于多智能体领域的高维性以及收集或模拟富含交互的群体-机器人演示数据的挑战，构建此类模型十分困难。尽管离线行人运动预测模型取得了重要进展，但由于紧密交互设置和对用户的新奇效应，将其性能迁移到真实机器人上并非易事。本文研究了最新最优运动预测模型（S-GAN）在人群导航任务中的效用。我们将该模型集成到模型预测控制器（MPC）中，并将其部署在一款自平衡机器人上，使其在实验室中面对多样化的人群行为。我们证明，虽然S-GAN的运动预测精度可迁移到真实世界，但其价值并未体现在以安全性和效率衡量的导航性能上；实际上，即使使用简单的恒速预测模型，MPC的性能也无显著差异，这表明可能需要大幅改进模型才能为人群导航任务带来实质性收益。实验录像可访问 https://youtu.be/mzFiXg8KsZ0。