CLEVER: 人間の指示によるロバストな意味知覚のためのストリームベース能動学習
CLEVER: Stream-based Active Learning for Robust Semantic Perception from Human Instructions
データストリーム上でDNNの失敗時に人間の指示を求め、ベイズ的定式化によりオンラインで適応させる能動学習システムを提案し、実ロボットで検証した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Jongseok Lee, Timo Birr, Rudolph Triebel, Tamim Asfour
分類: cs.RO
原文アブストラクト
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.
