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，协助完成各种任务。在此类环境中，机器人在工作时频繁遇到新物体，需要以开放式方式进行学习。此外，这类机器人必须能够识别大量物体类别。本文提出一种基于多表征的终身集成学习方法，以解决少样本物体识别问题。具体而言，我们基于深度表征与手工设计的3D形状描述子构建集成方法。为促进终身学习，每种方法均配备记忆单元，用于即时存储和检索物体信息。该模型适用于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。我们开展了大量实验，评估该方法在离线与开放式场景中的性能。除真实物体数据集外，我们还生成了一个包含90个物体27000个视图的大型合成家庭物体数据集用于评估。实验结果表明，该方法在在线少样本3D物体识别任务中具有有效性，且其性能优于当前最先进的开放式学习方法。此外，研究显示，虽然集成学习在离线场景中增益有限，但在终身少样本学习场景中具有显著优势。同时，我们在仿真与真实机器人环境中验证了该方法的效果，机器人能够从少量样本中快速学习新类别。