For real-world applications, robots will need to continually learn in their environments through limited interactions with their users. Toward this, previous works in few-shot class incremental learning (FSCIL) and active class selection (ACS) have achieved promising results but were tested in constrained setups. Therefore, in this paper, we combine ideas from FSCIL and ACS to develop a novel framework that can allow an autonomous agent to continually learn new objects by asking its users to label only a few of the most informative objects in the environment. To this end, we build on a state-of-the-art (SOTA) FSCIL model and extend it with techniques from ACS literature. We term this model Few-shot Incremental Active class SeleCtiOn (FIASco). We further integrate a potential field-based navigation technique with our model to develop a complete framework that can allow an agent to process and reason on its sensory data through the FIASco model, navigate towards the most informative object in the environment, gather data about the object through its sensors and incrementally update the FIASco model. Experimental results on a simulated agent and a real robot show the significance of our approach for long-term real-world robotics applications.

翻译：在现实应用中，机器人需要通过有限次与用户的交互在环境中持续学习。为此，以往关于少样本类增量学习（FSCIL）和主动类选择（ACS）的研究已取得显著成果，但均在受限设置下进行测试。因此，本文融合FSCIL与ACS的思想，提出一种新颖框架，使自主智能体能够通过请求用户仅标注环境中信息量最大的少数物体，持续学习新物体。具体而言，我们基于当前最先进的FSCIL模型，结合ACS文献中的技术对其进行扩展，将该模型命名为“少样本增量主动类选择”（FIASco）。进一步，我们在模型中集成基于势场导航的技术，构建完整框架，使智能体能够通过FIASco模型处理并推理其感知数据，导航至环境中信息量最大的物体，通过传感器采集该物体数据，并增量更新FIASco模型。在模拟智能体与真实机器人上的实验结果表明，本方法对长期真实世界机器人应用具有重要意义。