感じ、想像し、行動せよ：マルチモーダル知覚がモデルベース強化学習による自律レースを改善する
Sense, Imagine, Act: Multimodal Perception Improves Model-Based Reinforcement Learning for Head-to-Head Autonomous Racing
LiDARとRGBカメラを自己教師ありで融合し、Dreamerの世界モデルの精度を高めることで、未知コースでの1対1自律レースの堅牢性と勝率を向上させた。
著者: Elena Shrestha, Chetan Reddy, Hanxi Wan, Yulun Zhuang, Ram Vasudevan
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Model-based reinforcement learning (MBRL) techniques have recently yielded promising results for real-world autonomous racing using high-dimensional observations. MBRL agents, such as Dreamer, solve long-horizon tasks by building a world model and planning actions by latent imagination. This approach involves explicitly learning a model of the system dynamics and using it to learn the optimal policy for continuous control over multiple timesteps. As a result, MBRL agents may converge to sub-optimal policies if the world model is inaccurate. To improve state estimation for autonomous racing, this paper proposes a self-supervised sensor fusion technique that combines egocentric LiDAR and RGB camera observations collected from the F1TENTH Gym. The zero-shot performance of MBRL agents is empirically evaluated on unseen tracks and against a dynamic obstacle. This paper illustrates that multimodal perception improves robustness of the world model without requiring additional training data. The resulting multimodal Dreamer agent safely avoided collisions and won the most races compared to other tested baselines in zero-shot head-to-head autonomous racing.
