MapleGrasp: マスク誘導特徴プーリングによる言語駆動型効率的ロボット把持
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping
言語指示とRGB-D画像から把持姿勢を予測する際、CLIP特徴からセグメンテーションマスクを予測しその内部で特徴をプーリングすることで計算効率と精度を向上させ、大規模データセットRefGraspNetも提案した研究。
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著者: Vineet Bhat, Naman Patel, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami
分類: cs.RO
原文アブストラクト
Robotic manipulation of unseen objects via natural language commands remains challenging. Language driven robotic grasping (LDRG) predicts stable grasp poses from natural language queries and RGB-D images. We propose MapleGrasp, a novel framework that leverages mask-guided feature pooling for efficient vision-language driven grasping. Our two-stage training first predicts segmentation masks from CLIP-based vision-language features. The second stage pools features within these masks to generate pixel-level grasp predictions, improving efficiency, and reducing computation. Incorporating mask pooling results in a 7% improvement over prior approaches on the OCID-VLG benchmark. Furthermore, we introduce RefGraspNet, an open-source dataset eight times larger than existing alternatives, significantly enhancing model generalization for open-vocabulary grasping. MapleGrasp scores a strong grasping accuracy of 89\% when compared with competing methods in the RefGraspNet benchmark. Our method achieves comparable performance to larger Vision-Language-Action models on the LIBERO benchmark, and shows significantly better generalization to unseen tasks. Real-world experiments on a Franka arm demonstrate 73% success rate with unseen objects, surpassing competitive baselines by 11%. Code is provided in our github repository.
