視覚言語モデルを用いた直接接触許容型動作計画
Direct Contact-Tolerant Motion Planning With Vision Language Models
視覚言語モデルを点群認識とナビゲーションに統合し、移動可能な障害物がある環境での接触を許容する動作計画手法を提案した。
詳しい要約
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著者: He Li, Jian Sun, Chengyang Li, Guoliang Li, Qiyu Ruan, Shuai Wang, Chengzhong Xu
分類: cs.RO
原文アブストラクト
Navigation in cluttered environments often requires robots to tolerate contact with movable or deformable objects to maintain efficiency. Existing contact-tolerant motion planning (CTMP) methods rely on indirect spatial representations (e.g., prebuilt map, obstacle set), resulting in inaccuracies and a lack of adaptiveness to environmental uncertainties. To address this issue, we propose a direct contact-tolerant (DCT) planner, which integrates vision-language models (VLMs) into direct point perception and navigation, including two key components. The first one is VLM point cloud partitioner (VPP), which performs contact-tolerance reasoning in image space using VLM, caches inference masks, propagates them across frames using odometry, and projects them onto the current scan to generate a contact-aware point cloud. The second innovation is VPP guided navigation (VGN), which formulates CTMP as a perception-to-control optimization problem under direct contact-aware point cloud constraints, which is further solved by a specialized deep neural network (DNN). We implement DCT in Isaac Sim and a real car-like robot, demonstrating that DCT achieves robust and efficient navigation in cluttered environments with movable obstacles, outperforming representative baselines across diverse metrics. The code is available at: https://github.com/ChrisLeeUM/DCT.
