Easy-IIL: アシスタント専門家による対話型模倣学習の人間の操作負担軽減
Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via Assistant Experts
対話型模倣学習において、モデルベースの模倣手法をアシスタント専門家として活用し、人間の介入を最小限に抑えつつ性能を維持するフレームワークを提案した。
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？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Chengjie Zhang, Chao Tang, Wenlong Dong, Dehao Huang, Aoxiang Gu, Hong Zhang
分類: cs.RO
原文アブストラクト
Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior work primarily focuses on reducing human effort in passive monitoring rather than active operation. Interestingly, structured model-based imitation approaches achieve comparable performance with significantly fewer demonstrations than end-to-end imitation learning policies in the low-data regime. However, these methods are typically surpassed by end-to-end policies as the data increases. Leveraging this insight, we propose Easy-IIL, a framework that utilizes off-the-shelf model-based imitation methods as an assistant expert to replace active human operation for the majority of data collection. The human expert only provides a single demonstration to initialize the assistant expert and intervenes in critical states where the task is approaching failure. Furthermore, Easy-IIL can maintain IIL performance by preserving both offline and online data quality. Extensive simulation and real-world experiments demonstrate that Easy-IIL significantly reduces human operational burden while maintaining performance comparable to mainstream IIL baselines. User studies further confirm that Easy-IIL reduces subjective workload on the human expert. Project page: https://sites.google.com/view/easy-iil
