シミュレーションと実世界の最良を活かす：シミュレーションでの制御学習と実世界での知覚による分離型視覚運動マニピュレーション
Best of Sim and Real: Decoupled Visuomotor Manipulation via Learning Control in Simulation and Perception in Real
制御方策をシミュレーションで学習し、知覚のみを実環境で適応させる分離フレームワークを提案。10〜20回の実演で高いデータ効率と分布外汎化を実現した。
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？
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6. 次に読むべき論文は？
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著者: Jialei Huang, Zhaoheng Yin, Yingdong Hu, Shuo Wang, Xingyu Lin, Yang Gao
分類: cs.RO
原文アブストラクト
Sim-to-real transfer remains a fundamental challenge in robot manipulation due to the entanglement of perception and control in end-to-end learning. We present a decoupled framework that learns each component where it is most reliable: control policies are trained in simulation with privileged state to master spatial layouts and manipulation dynamics, while perception is adapted only at deployment to bridge real observations to the frozen control policy. Our key insight is that control strategies and action patterns are universal across environments and can be learned in simulation through systematic randomization, while perception is inherently domain-specific and must be learned where visual observations are authentic. Unlike existing end-to-end approaches that require extensive real-world data, our method achieves strong performance with only 10-20 real demonstrations by reducing the complex sim-to-real problem to a structured perception alignment task. We validate our approach on tabletop manipulation tasks, demonstrating superior data efficiency and out-of-distribution generalization compared to end-to-end baselines. The learned policies successfully handle object positions and scales beyond the training distribution, confirming that decoupling perception from control fundamentally improves sim-to-real transfer.
