指示分解によるマルチ粒度言語ガイド模倣学習
Multi-Granularity Language-Guided Imitation Learning via Instruction Decomposition
タスク全体の言語指示を細かいサブタスク指示に分解し、多段階の操作タスクにおける模倣学習の効率と性能を向上させる手法を提案した。
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？
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6. 次に読むべき論文は？
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著者: Yi-Pei Chiu, Wei-Ta Chu
分類: cs.CV
原文アブストラクト
Using language instructions as conditions to guide robot policy learning has recently become an important research domain. However, existing language-guided policy learning methods typically use an overall task description to guide the entire demonstration trajectory. For manipulation tasks involving multiple execution stages, these methods assign the same language description to different subtasks, making it difficult to distinguish the behaviors required at different stages. In this work, we propose a multi-granularity language guidance method based on instruction decomposition. The proposed method decomposes an overall task description into more fine-grained, concrete subtask-level language instructions, thereby enhancing learning efficiency and improving performance. We evaluate the proposed method in the setting of multi-task imitation learning and validate its effectiveness.