ロボットは誰に教わったのか？オンライン学習するロボット方策から特定ユーザーの修正を除去する
Who Taught the Robot That? Removing One User's Corrections from a Robot Policy That Learns Online
展開後も学習を続けるロボットから特定のユーザーの影響を消すには、そのユーザーがどう修正したか（操作の引き継ぎか微調整か）に依存することをシミュレーションで示し、除去可能性の条件を明らかにした。
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？
※ 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: Eshika Pathak, Leela Krishna
分類: cs.RO, cs.HC
原文アブストラクト
A robot that keeps learning after deployment is corrected by several people in turn, and one of them may later ask to be removed. We show that whether the robot can forget that person depends on how people correct it. The goal of removal is the robot that would exist if the person had never been there, which we compute exactly in two simulated tasks with scripted users. When users correct the robot by taking over control, an ordered log of training updates is enough: retraining on the remaining sessions in their original order gives a robot as close to the goal as training noise allows. Deleting the person's data and retraining in one pass does not, even when the other users' corrections do not depend on the removed person, because it loses the order of training. When users only nudge the robot's action, the other users' corrections carry the removed person's influence, and no method we tested recovers the goal robot. Continuing to train without the person removes most of their habits, but not what their data in the training buffer keep alive. The log can identify one clearly harmful user, but not rank users by influence better than counting their corrections.