世界モデル中心の自動運転レーシングエージェントによる身体化知能の認知的・物理的限界への挑戦
Toward the Cognitive--Physical Limits of Embodied Intelligence through a World-Model-Centric Autonomous Racing Agent
自動運転レーシングを題材に、世界モデルを中心としたエージェントが高速走行中の認知と物理の限界を同時に学習・洗練する手法を提案し、実車データとシミュレーションで有効性を示した。
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: Zitong Shan, Baichuan Lou, Yanxin Zhou, Shuge Wu, Xianqi He, Bolin Zhao, Sheng Zhao, Zhouheng Li, Chee Kiong Ong, King Ho Holden Li, Chen Lv
分類: cs.RO
原文アブストラクト
Embodied artificial intelligence aims to develop agents that perceive, reason, and act through continuous interaction with the physical world. However, most embodied systems are still evaluated within conservative safety margins or moderate interaction regimes, leaving their capability boundaries under extreme conditions insufficiently understood. Autonomous racing provides a stringent testbed by combining high-frequency localization and perception, adversarial interaction, near-saturated vehicle dynamics, and strict safety constraints. Existing systems push high-speed performance but rarely model and refine cognitive and physical limits jointly. Here we show that a world-model-centric autonomous racing agent provides a concrete step toward exploring these coupled limits. The framework learns predictive world models from near-limit successes and failures to capture interaction evolution, ego dynamics, and feasible-motion boundaries, coupling world-state construction, future-aware reasoning, and near-limit control in a closed-loop refinement process. Training data were collected from real-vehicle autonomous racing, where the onboard system maintained robust localization and perception at speeds up to 256.3 km/h and peak lateral acceleration of 26.8 m/s$^2$. In full-scale simulated racing, the well trained world-model-centric agent achieves an 88.3% interaction success rate across various challenging simulated racing scenarios. Closed-loop refinement of the world model and policy further improved utilization of cognitive-physical limits, recovery from failure modes, and generalization across varying conditions and unseen circuits. These results suggest a boundary-aware methodology in which world models help embodied agents represent, predict, and continually refine their capability boundaries for safer real-world deployment.
関連論文
- 空間認識型適応軌道最適化と制御器誘導フィードバックによる自動運転レーシング自動運転レーシング
- 速度予測を組み込んだ局所軌道計画によるデータ駆動型アグレッシブ自動レーシング自動運転レーシング
- 自動運転レーシングソフトウェアとプロレーサーの比較評価自動運転レーシング
