Learning socially-aware motion representations is at the core of recent advances in human trajectory forecasting and robot navigation in crowded spaces. Despite promising progress, existing neural motion models often struggle to generalize in closed-loop operations (e.g., output colliding trajectories), when the training set lacks examples collected from dangerous scenarios. In this work, we propose to address this issue via contrastive learning with negative data augmentation. Concretely, we introduce a social contrastive loss that encourages the encoded motion representation to preserve sufficient information for distinguishing a positive future event from a set of negative ones. We explicitly draw these negative samples based on our domain knowledge of unfavorable circumstances in the multi-agent context. Experimental results show that the proposed method dramatically reduces the collision rates of recent trajectory forecasting, behavioral cloning and reinforcement learning algorithms, outperforming current state-of-the-art models on several benchmarks. Our method makes few assumptions about neural architecture designs, and hence can be used as a generic way to promote the robustness of neural motion models.
翻译:学习社会觉悟运动演示是人类轨迹预测和拥挤空间机器人导航方面最近进展的核心。尽管取得了有希望的进展,但现有的神经运动模型往往难以在封闭环状操作(例如,产出碰撞轨迹)中推广,因为培训组缺乏从危险情景中收集的范例。在这项工作中,我们建议通过对比学习和负面数据增强来解决这一问题。具体地说,我们引入了一种社会对比损失,鼓励编码运动演示保存足够的信息,以区分积极的未来事件和一系列负面事件。我们根据我们对多试剂环境中不受欢迎的情况的域性知识,明确绘制这些负面样本。实验结果显示,拟议的方法大大降低了近期轨迹预测、行为克隆和强化学习算法的碰撞率,在几个基准上优于目前的艺术模型。我们的方法对神经结构设计提出了很少的假设,因此可以用作促进神经运动模型稳健的通用方法。