実世界蒸留による観察アライメントを用いた画像ベースマニピュレーション方策学習
ReDOA: Real-World Distillation via Observation Alignment for Image-Based Manipulation Policy Learning
シミュレーションで訓練した状態ベース教師方策を実機で実行し、その制御知識を画像ベースの生徒方策へ蒸留する枠組みを提案。観察アライメントでデータ収集と実運用間の観察ずれを解消し、人間の実演なしに効率的な実世界方策を学習できる。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Kotaro Kajitsuka, Masaki Murooka, Fumio Kanehiro
分類: cs.RO
原文アブストラクト
Data-driven learning for general-purpose robotic manipulation presents a fundamental trade-off between data-collection cost and adaptability to real-world environments. Imitation learning (IL) requires costly human demonstrations, whereas simulation-based learning is vulnerable to sim-to-real gaps in dynamics and sensor observations. We propose ReDOA (Real-World Distillation via Observation Alignment), a framework that decouples control learning from perception learning to learn image-based policies in the real world. A state-based teacher policy trained in simulation is executed on a real robot, and its control knowledge is distilled into an image-based student policy using the collected trajectories. Observation Alignment resolves observation mismatches between data collection and deployment. This framework collects successful, efficiently executed trajectories paired with real-world visual observations, without requiring demonstrations from human experts. In real-robot experiments on three non-prehensile manipulation tasks, including bimanual manipulation, our method achieves better overall performance when jointly considering task success and execution efficiency than both IL based on human demonstrations and policy distillation in simulation. https://isri-aist.github.io/redoa-project-page/
