単眼視覚に基づく把持のための制御フレームワーク
Monocular Vision Based Control Framework for Grasping
RGB画像のみを用いて、柔軟物体と剛体物体の両方を単一の制御パイプラインで把持するための、言語ベースの剛性推定と視覚的変形モニタリングを組み合わせたフレームワークを提案した。
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: Shail Jadav, Dongheui Lee
分類: cs.RO
原文アブストラクト
Grasping in unstructured environments requires handling objects with widely different mechanical properties, from soft and deformable items to rigid everyday objects. Most existing approaches address these categories separately and often rely on tactile sensing, object-specific models, or specialized grippers. In this paper, we present a unified monocular vision-based grasping framework that targets both soft and rigid objects within a single control pipeline, using only RGB input and a position-controlled gripper. The proposed system combines open-vocabulary object detection, image segmentation, boundary-aware point assignment, real-time point tracking, and monocular depth estimation to recover object motion and geometry from visual observations. A key component of the framework is a language-based stiffness estimation model that infers an object's expected compliance from its semantic description and provides an object-level prior for selecting the grasping strategy before contact. For deformable objects, grasp adaptation is governed by a Procrustes-based dissimilarity measure computed from tracked keypoints, which acts as a visual proxy for deformation. For rigid objects, the gripper width is regulated through the scaling of tracked point distances. We validate the proposed method in real-world pick-and-place experiments on a Franka Emika Research 3 arm using objects with substantially different mechanical properties, including lettuce, fresh mozzarella cheese, croissants, paper towels, and hard plastic bottles. Results demonstrate that the framework achieves stable grasping across both soft and rigid objects using visual feedback alone, highlighting a practical, sensor-efficient, and generalizable approach for food handling and household manipulation.
