物体特性と空間認識を考慮した変形可能な線状物体の操作ポリシー
PS-DLO: Property- and Spatially Aware Deformable Linear Object Manipulation Policy
物体の物理的特性を画像テキスト学習で推定し、将来の観測から得た空間情報を方策に蒸留することで、単腕・双腕ロボットによる線状物体の多タスク操作の成功率と視覚変化への頑健性を向上させた研究。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Mengdi Li, Boxian Yao, Xiyu Zhu, Yifan Yang, Yunfan Lou, Xuan Han, Tao Liu, Zhibo Pang
分類: cs.RO
原文アブストラクト
Deformable linear objects (DLO) pose a coupled perception and control challenge: their slender geometry makes precise localization difficult, while their deformations depend on object-specific physical properties. To address this challenge, we propose PS-DLO, a property- and spatially aware DLO manipulation policy. First, contrastive image-text learning maps the initial observation to categorical descriptions of DLO properties, providing explicit physical and structural priors for action generation. Second, SAM3 extracts mask-guided features from future observations, which are distilled into the policy's action expert to encode task-relevant DLO regions and their subsequent configurations. We evaluate PS-DLO on nine real-world tasks across single-arm and bimanual platforms, training a shared multi-task policy for each embodiment. PS-DLO achieves an average success rate of 68.9%, exceeding the strongest baseline by 20.0 percentage points. In generalization experiments, PS-DLO achieves higher success rates than the baseline in six of seven visual-shift conditions on butterfly-knot untying. These results demonstrate that combining object-property priors with future spatial supervision improves multi-task DLO manipulation and robustness to visual variation.
