Service robots are increasingly integrating into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them in an open-ended fashion. Furthermore, such robots must be able to recognize a wide range of object categories. In this paper, we present a lifelong ensemble learning approach based on multiple representations to address the few-shot object recognition problem. In particular, we form ensemble methods based on deep representations and handcrafted 3D shape descriptors. To facilitate lifelong learning, each approach is equipped with a memory unit for storing and retrieving object information instantly. The proposed model is suitable for open-ended learning scenarios where the number of 3D object categories is not fixed and can grow over time. We have performed extensive sets of experiments to assess the performance of the proposed approach in offline, and open-ended scenarios. For evaluation purposes, in addition to real object datasets, we generate a large synthetic household objects dataset consisting of 27000 views of 90 objects. Experimental results demonstrate the effectiveness of the proposed method on online few-shot 3D object recognition tasks, as well as its superior performance over the state-of-the-art open-ended learning approaches. Furthermore, our results show that while ensemble learning is modestly beneficial in offline settings, it is significantly beneficial in lifelong few-shot learning situations. Additionally, we demonstrated the effectiveness of our approach in both simulated and real-robot settings, where the robot rapidly learned new categories from limited examples. A video of our experiments is available online at: https://youtu.be/nxVrQCuYGdI.

服务机器人越来越多地融入我们的日常生活，帮助我们完成各种任务。在这样的环境中，机器人在环境中工作时经常面对新的物体，并且需要以开放式的方式学习它们。此外，此类机器人必须能够识别广泛的物体类别。在本文中，我们提出了一种基于多重表示的终身集成学习方法，以解决少样本目标识别问题。特别是，我们基于深度表示和手工制作的 3D 形状描述符形成集成方法。为了促进终身学习，每种方法都配备了一个存储单元，用于立即存储和检索对象信息。所提出的模型适用于开放式学习场景，其中 3D 对象类别的数量不固定，并且会随着时间的推移而增长。我们进行了大量的实验来评估所提出的方法在离线和开放场景中的性能。出于评估目的，除了真实物体数据集之外，我们还生成了一个大型综合家居物体数据集，其中包含 90 个物体的 27000 个视图。实验结果证明了该方法在在线少镜头 3D 对象识别任务上的有效性，以及其优于最先进的开放式学习方法的性能。此外，我们的结果表明，虽然集成学习在离线环境中具有一定的益处，但它在终身几次学习的情况下却非常有益。此外，我们还证明了我们的方法在模拟和真实机器人设置中的有效性，其中机器人从有限的示例中快速学习新类别。我们的实验视频可在线观看： https: //youtu.be/nxVrQCuYGdI。