让机器人实时学习人类指令，提升视觉理解的鲁棒性。
CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
- 基于贝叶斯框架，用先验知识指导在线学习。
- 在真实机器人上实现流式主动学习，首次验证可行性。
- 适合需要持续优化感知能力的机器人应用。
我们提出CLEVER，一个面向深度神经网络（DNN）的流式主动学习系统，用于实现鲁棒的语义感知。对于持续到达的数据流，当系统检测到失败时会请求人类干预，并根据人类指令在线更新DNN模型。该方法使系统最终能完成给定的语义感知任务。核心贡献在于设计了一套满足多项理想特性的系统架构，其关键在于采用贝叶斯公式，通过先验知识编码领域知识。实验方面，我们不仅验证了设计合理性，还通过用户研究和对人形及可变形物体的测试，展示了系统的实际能力。据我们所知，这是首个在真实机器人上实现流式主动学习的研究，为提升基于DNN的语义感知鲁棒性提供了实证支持。
原文摘要 · Abstract (English)
We propose CLEVER, an active learning system for robust semantic perception with Deep Neural Networks (DNNs). For data arriving in streams, our system seeks human support when encountering failures and adapts DNNs online based on human instructions. In this way, CLEVER can eventually accomplish the given semantic perception tasks. Our main contribution is the design of a system that meets several desiderata of realizing the aforementioned capabilities. The key enabler herein is our Bayesian formulation that encodes domain knowledge through priors. Empirically, we not only motivate CLEVER's design but further demonstrate its capabilities with a user validation study as well as experiments on humanoid and deformable objects. To our knowledge, we are the first to realize stream-based active learning on a real robot, providing evidence that the robustness of the DNN-based semantic perception can be improved in practice. The project website can be accessed at https://sites.google.com/view/thecleversystem.
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