TILDE: 身体性基盤モデルからの教師参加型蒸留
TILDE: Teacher-in-the-Loop Distillation from Embodied Foundation Models
大規模行動事前学習済みの身体性基盤モデルから、事前学習なしのポリシーへ能力を転移する蒸留手法を提案し、報酬モデルで教師介入を自動化するデータ収集パイプラインにより実世界3タスクで最大4.1倍の性能向上を実現した。
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: Yuxiang Lu, Yantong Liu, Jinghua Hou, Xueli Sun, Ruihua Han, Zhe Liu, Hengshuang Zhao
分類: cs.RO, cs.LG
原文アブストラクト
Large-scale action pretraining equips Embodied Foundation Models (EFMs) with strong manipulation capabilities, with particular strengths in generalization and closed-loop robustness, but reproducing such pretraining requires substantial robot data and computation. To transfer these capabilities from pretrained EFMs to policies without action pretraining, we introduce TILDE (Teacher-In-the-Loop Distillation from EFMs), which synergizes on-policy distillation at student-induced states with learning from teacher-executed recovery trajectories. We further propose AutoDAgger, an autonomous data aggregation pipeline in which a robotic reward model is utilized to monitor task progress and trigger teacher intervention, thereby collecting recovery experience without human monitoring or manual control. AutoDAgger accelerates recovery-trajectory collection by more than $2\times$ compared with the human-in-the-loop method. By unifying learning from demonstrations, on-policy teacher supervision, and teacher's corrective behavior, TILDE improves task performance by up to $4.1\times$ over behavior cloning across three real-world tasks and supports effective cross-architecture distillation.
