システム同定を用いたASVのSim-to-Real強化学習
Sim-to-Real RL for ASVs using SysID
未知の車両ダイナミクスから始めて、CADモデルと短い実海域軌跡のみで流体力学・スラスタパラメータを推定し、強化学習方策を訓練するASVシミュレータとパイプラインを提案。BlueBoatでの実機実験で経路追従と定点保持のゼロショットsim-to-real転移に成功した。
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: Cody Sheltraw, Tsimafei Lazouski, Maani Ghaffari, Alan Papalia
分類: cs.RO
原文アブストラクト
Autonomous Surface Vehicles (ASVs) operating in dynamic marine environments require robust control policies for tasks such as path following and station keeping, making reinforcement learning (RL) a promising alternative to classical controllers. However, existing ASV simulators rarely support parallel environments for RL training. Such existing simulators require accurate hydrodynamic modeling from computational fluid dynamics solvers or towing tank tests for setting hydrodynamic parameters to address the sim-to-real gap. To address these challenges, we present an ASV simulator and accompanying pipeline that enables training policies starting from unknown vehicle dynamics. Our framework uses only a CAD model and brief set of open-water field trajectories for approximating and refining both hydrodynamic and thruster parameters. Real-world deployments on a BlueBoat ASV demonstrate successful zero-shot sim-to-real transfer in path following and station-keeping tasks without prior hydrodynamic and propeller information.
