見せるだけでいい：人間の動画を見て新規物体を検出する
Show, Don't Tell: Detecting Novel Objects by Watching Human Videos
人間のデモ動画から自動でデータセットを作り、その物体専用の検出器を自己教師ありで学習する手法を提案。言語による複雑な説明を不要にし、ロボットのタスク成功率を向上させた。
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？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: James Akl, Jose Nicolas Avendano Arbelaez, James Barabas, Jennifer L. Barry, Kalie Ching, Noam Eshed, Jiahui Fu, Michel Hidalgo, Andrew Hoelscher, Tushar Kusnur, Andrew Messing, Zachary Nagler, Brian Okorn, Mauro Passerino, Tim J. Perkins, Eric Rosen, Ankit Shah, Tanmay Shankar, Scott Shaw
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
How can a robot quickly identify and recognize new objects shown to it during a human demonstration? Existing closed-set object detectors frequently fail at this because the objects are out-of-distribution. While open-set detectors (e.g., VLMs) sometimes succeed, they often require expensive and tedious human-in-the-loop prompt engineering to uniquely recognize novel object instances. In this paper, we present a self-supervised system that eliminates the need for tedious language descriptions and expensive prompt engineering by training a bespoke object detector on an automatically created dataset, supervised by the human demonstration itself. In our approach, "Show, Don't Tell," we show the detector the specific objects of interest during the demonstration, rather than telling the detector about these objects via complex language descriptions. By bypassing language altogether, this paradigm enables us to quickly train bespoke detectors tailored to the relevant objects observed in human task demonstrations. We develop an integrated on-robot system to deploy our "Show, Don't Tell" paradigm of automatic dataset creation and novel object-detection on a real-world robot. Empirical results demonstrate that our pipeline significantly outperforms state-of-the-art detection and recognition methods for manipulated objects, leading to improved task completion for the robot.
