With the increasing availability and affordability of personal robots, they will no longer be confined to large corporate warehouses or factories but will instead be expected to operate in less controlled environments alongside larger groups of people. In addition to ensuring safety and efficiency, it is crucial to minimize any negative psychological impact robots may have on humans and follow unwritten social norms in these situations. Our research aims to develop a model that can predict the movements of pedestrians and perceptually-social groups in crowded environments. We introduce a new Social Group Long Short-term Memory (SG-LSTM) model that models human groups and interactions in dense environments using a socially-aware LSTM to produce more accurate trajectory predictions. Our approach enables navigation algorithms to calculate collision-free paths faster and more accurately in crowded environments. Additionally, we also release a large video dataset with labeled pedestrian groups for the broader social navigation community. We show comparisons with different metrics on different datasets (ETH, Hotel, MOT15) and different prediction approaches (LIN, LSTM, O-LSTM, S-LSTM) as well as runtime performance.

翻译：随着个人机器人的日益普及和价格亲民化，它们将不再局限于大型企业仓库或工厂，而是被期望在较少受控的环境中与更大规模的人群协同运作。在确保安全性和效率的同时，最大程度减少机器人对人类可能产生的负面心理影响，并遵守这些场景中不成文的社会规范至关重要。本研究旨在开发一种能够预测拥挤环境中行人及具感知社会群体移动轨迹的模型。我们提出了一种新型社会群体长短期记忆模型（SG-LSTM），该模型通过社会感知LSTM对密集环境中的人类群体及其交互行为进行建模，从而生成更精确的轨迹预测。我们的方法使导航算法能够在拥挤环境中更快、更准确地计算无碰撞路径。此外，我们还发布了一个包含标注行人群体的大规模视频数据集，以服务于更广泛的社会导航研究领域。通过不同数据集（ETH、Hotel、MOT15）和不同预测方法（LIN、LSTM、O-LSTM、S-LSTM）的对比实验，我们展示了模型在各项指标及运行时性能上的表现。