LiteNWM: 実環境でのオンボード視覚ナビゲーションのための効率的な潜在世界モデル
LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild
複数の候補軌道の将来表現を共有エンコーダでまとめて予測し、学習済みスコアラで軌道選択する軽量な潜在世界モデルを提案。実機ナビゲーション成功率を43.3%から83.3%に改善した。
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: Linkai Liu, Yuntian Zhang, Zhenshan Bing, Chen Chen, Lingjuan Lyu, Shangguang Wang, Mengwei Xu, Dongqi Cai
分類: cs.RO
原文アブストラクト
Direct visual navigation policies generate trajectories efficiently but do not explicitly evaluate their future consequences. Generative navigation world models provide this foresight through visual rollouts, which are costly when evaluating multiple candidates. We present LiteNWM, a latent navigation world model that shares visual encoding across candidates and jointly predicts their action-conditioned future representations at multiple horizons, while a learned scorer uses these predictions to select trajectories. In offline evaluations on RECON, SCAND, and SACSoN, LiteNWM reduces macro-averaged trajectory error by 17.56% relative to NoMaD+NWM-XL and achieves a 128.00-fold end-to-end speedup on an RTX 5090. The same evaluator transfers from NoMaD to MBRA without proposer-specific retraining, reducing MBRA's macro-averaged trajectory error by 16.2%. In real-robot experiments in unseen indoor and outdoor environments, LiteNWM improves navigation success from 43.3% to 83.3% relative to NoMaD. These results demonstrate that LiteNWM can be deployed for future-aware planning and closed-loop navigation on a physical robot.