Autonomous mobile robots require accurate human motion predictions to safely and efficiently navigate among pedestrians, whose behavior may adapt to environmental changes. This paper introduces a self-supervised continual learning framework to improve data-driven pedestrian prediction models online across various scenarios continuously. In particular, we exploit online streams of pedestrian data, commonly available from the robot's detection and tracking pipeline, to refine the prediction model and its performance in unseen scenarios. To avoid the forgetting of previously learned concepts, a problem known as catastrophic forgetting, our framework includes a regularization loss to penalize changes of model parameters that are important for previous scenarios and retrains on a set of previous examples to retain past knowledge. Experimental results on real and simulation data show that our approach can improve prediction performance in unseen scenarios while retaining knowledge from seen scenarios when compared to naively training the prediction model online.
翻译:自主移动机器人需要准确的人类运动预测,以安全和高效地在行人之间航行,行人的行为可以适应环境变化。本文件介绍了一个自我监督的持续学习框架,以不断改进不同情景的在线数据驱动行人预测模型。特别是,我们利用从机器人探测和跟踪管道中常见到的行人数据在线流,完善预测模型及其在不可见情景中的性能。为了避免忘记以往所学概念,一个被称为灾难性遗忘的问题,我们的框架包括了正规化损失,以惩罚对以往情景至关重要的模型参数变化,并重新使用一系列以往实例来保留以往知识。真实和模拟数据的实验结果表明,我们的方法可以改进在不可见情景中的预测,同时保留在对在线预测模型进行天真培训时从所见情景中获取的知识。