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：随着个人机器人日益普及且成本降低，它们不再局限于大型企业仓库或工厂，而是需要在人类群体密集、控制较少的环境中运行。除确保安全与效率外，最小化机器人对人类造成的负面心理影响并遵循非正式社会规范至关重要。本研究旨在开发一种能预测拥挤环境中行人及感知社交群体移动轨迹的模型。我们提出了一种新型社交群体长短期记忆（SG-LSTM）模型，该模型通过社交感知LSTM对密集环境中的群体及交互行为进行建模，以实现更精确的轨迹预测。本方法使导航算法能够在拥挤环境中更快、更精准地计算无碰撞路径。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，以供更广泛的社交导航社区使用。我们在不同数据集（ETH、Hotel、MOT15）上，将本方法与多种预测方法（LIN、LSTM、O-LSTM、S-LSTM）进行指标对比，并验证了运行效率。