狭い方策に潜む悪魔：運転VLAモデルの探索を解き放つ
Devil is in Narrow Policy: Unleashing Exploration in Driving VLA Models
自動運転の模倣学習が探索を狭め、強化学習の性能を制限する問題を特定し、軌道拡張と多様性重視の報酬設計で解決するCurious-VLAを提案した。
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: Canyu Chen, Yuguang Yang, Zhewen Tan, Yizhi Wang, Ruiyi Zhan, Haiyan Liu, Xuanyao Mao, Jason Bao, Xinyue Tang, Linlin Yang, Bingchuan Sun, Yan Wang, Baochang Zhang
分類: cs.CV, cs.RO
原文アブストラクト
We identify a fundamental Narrow Policy limitation undermining the performance of autonomous VLA models, where driving Imitation Learning (IL) tends to collapse exploration and limit the potential of subsequent Reinforcement Learning (RL) stages, which often saturate prematurely due to insufficient feedback diversity. Thereby, we propose Curious-VLA, a framework that alleviates the exploit-explore dilemma through a two-stage design. During IL, we introduce a Feasible Trajectory Expansion (FTE) strategy to generate multiple physically valid trajectories and a step-wise normalized trajectory representation to adapt this diverse data. In the RL stage, we present Adaptive Diversity-Aware Sampling (ADAS) that prioritizes high-diversity samples and introduce Spanning Driving Reward (SDR) with a focal style weighting to amplify reward's value span for improving sensitivity to driving quality. On the Navsim benchmark, Curious-VLA achieves SoTA results (PDMS 90.3, EPDMS 85.4) and a Best-of-N PDMS of 94.8, demonstrating its effectiveness in unlocking the exploratory potential of VLA models. Code: https://github.com/Mashiroln/curious_vla.git.
