Service robots are integrating more and more 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 the evaluation purpose, 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.

翻译：服务机器人正越来越多地融入我们的日常生活，协助完成各类任务。在此类环境中，机器人在工作时会频繁遭遇新物体，并需要以开放式的方式学习它们。此外，这样的机器人必须能够识别广泛的对象类别。在本文中，我们提出了一种基于多表征的终身集成学习方法，以解决小样本物体识别问题。具体而言，我们基于深度表征和手工设计的3D形状描述子构建了集成方法。为促进终身学习，每种方法都配备了一个存储器单元，用于即时存储和检索物体信息。所提模型适用于3D物体类别数量不固定且可随时间增长的开放式学习场景。我们进行了大量实验，以评估所提方法在离线场景和开放式场景中的性能。为了评估目的，除真实物体数据集外，我们还生成了一个大型合成家庭物体数据集，包含90个物体的27000个视图。实验结果表明，所提方法在在线小样本3D物体识别任务上具有有效性，且在性能上优于当前最先进的开放式学习方法。此外，我们的结果还表明，虽然集成学习在离线设置中仅带来适度收益，但在终身小样本学习场景中具有显著优势。同时，我们分别在仿真和真实机器人环境中验证了方法的有效性，机器人能够从有限样例中快速学习新类别。