実時間でのゼロからの自律化：非構造環境における動力学のオンライン適応
Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments
関数エンコーダと再帰的最小二乗法を組み合わせ、ストリーミングオドメトリから動力学モデルをオンラインで適応させる手法を提案し、氷上を含む非構造地形でのナビゲーション性能を向上させた。
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: William Ward, Sarah Etter, Jesse Quattrociocchi, Christian Ellis, Adam J. Thorpe, Ufuk Topcu
分類: cs.RO
原文アブストラクト
Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes, such as a sudden transition to ice, create dynamics shifts that can destabilize planners unless the model adapts in real-time. We present a method for online adaptation that combines function encoders with recursive least squares, treating the function encoder coefficients as latent states updated from streaming odometry. This yields constant-time coefficient estimation without gradient-based inner-loop updates, enabling adaptation from only a few seconds of data. We evaluate our approach on a Van der Pol system to highlight algorithmic behavior, in a Unity simulator for high-fidelity off-road navigation, and on a Clearpath Jackal robot, including on a challenging terrain at a local ice rink. Across these settings, our method improves model accuracy and downstream planning, reducing collisions compared to static and meta-learning baselines.
