散らかった環境でゼロショット対話型ナビゲーションを実現するMessyNav
MessyNav: Zero-Shot Navigation via Manipulation in Messy Environments
視覚言語モデルによるアフォーダンス推論を用い、障害物を操作して道を切り開くゼロショットの対話型ナビゲーション手法を提案し、実機実験で成功率を27ポイント改善した。
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: Kenneth Llontop, Cherie Ho, Dylan Zhou, Anuva Banwasi, Priya Sundaresan, Carlota Parés-Morlans, Jeannette Bohg
分類: cs.RO, cs.CV
原文アブストラクト
Real-world environments are messy, with paths often blocked by everyday objects like chairs and toys, requiring robots to perform interactive navigation by manipulating obstacles to clear a path. Existing methods rely on extensive simulation-based learning or per-scene instructions specifying which objects to avoid or manipulate. They struggle to generalize across novel objects, diverse environments, and varied contexts such as a cart of fragile glassware that should be avoided while an empty one can be pushed. We formalize this problem as open-world interactive navigation and present MessyNav, a zero-shot framework based on vision-language models (VLMs). At its core, MessyNav performs explicit VLM-based affordance reasoning before task planning to decide whether to swipe, pick up, or avoid an object and produce a high-level plan. Evaluated on a mobile manipulator across real-world indoor and outdoor environments, MessyNav improves success rate by 27 percentage points (pp) over the strongest baseline in real-robot trials and achieves statistically significant gains in a larger-scale offline evaluation, without task-specific training.