地形を横断する高速MPCのための汎用車両モデルの適応
Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains
汎用の前方動力学予測モデルを、限られた実データと環境固有のシステム同定による合成ロールアウトで微調整し、特定車両向けに特化させつつ地形間の汎化を保つ手法OptCarを提案。3つの地形と外れ値のカート牽引タスクでの閉ループMPC実験で、最高速度6m/sでの追従誤差を約55%削減した。
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？
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4. どうやって有効だと検証した？
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著者: Rwik Rana, Jesse Quattrociocchi, Christian Ellis, Nathan Tsoi, Garrett Warnell, Joydeep Biswas
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
High-speed off-road autonomy requires precise closed-loop control for a target vehicle while remaining robust across changing terrains. Recent forward kinodynamic (FKD) prediction foundation models suggest a promising path, starting from a generalist model and specializing it to the target platform. However, effective specialization remains challenging, as it often requires substantial real-world data, and models adapted to one setting can still overfit to specific terrains or driving regimes. We present OptCar (Optimized Car), a recipe for bridging the gap from generalist to specialist FKD models that preserves cross-terrain generalization while optimizing performance for a specific vehicle. $\texttt{OptCar}$ introduces a history-conditioned dynamics adaptation module that encodes recent state-action observations into a dynamics context token, and then fine-tunes the generalist model using limited real-world data together with targeted synthetic rollouts from environment-specific system identification. In closed-loop model predictive control (MPC) experiments across three terrains and an out-of-distribution cart-pulling task, the largest gains appear at 6~m/s, the highest speed evaluated and the regime in which slip dominates tracking error. On vegetation and dirt, the most slip-diverse terrain, OptCar reduces 6~m/s trajectory tracking error by roughly 55% relative to a fine-tuned AnyCar baseline, and remains the most accurate even when an unseen cart payload changes the dynamics. With only 5 minutes of real data per terrain, OptCar is competitive on road with a specialist trained on 30 minutes of road data, and substantially outperforms it once the terrain changes.
関連論文
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