提出两种针对非对称夹爪的2D和3D抓取规划方法，提升抓取成功率。
2D and 3D Grasp Planners for the GET Asymmetrical Gripper
- 基于单视角RGB-D图像，结合新采样策略与Ferrari-Canny度量进行快速抓取规划
- 2D方法比边界框基线提升40%以上的抓取成功率、抗抖动性和抗力性能
- 3D方法精度略优但耗时17秒，适合高精度场景；2D方法仅683毫秒，适合实时应用
本文提出GET-2D-1.0，一种基于单视图RGB-D图像的快速抓取规划器，采用Ferrari-Canny度量与新型采样策略，适用于非对称夹爪。同时提出GET-3D-1.0，基于3D夹爪模型与射线追踪的网格方法。物理实验表明，GET-2D-1.0相比边界框基线在抓取成功率、抗抖动性和抗力性能上均提升超40%。GET-3D-1.0在抓取成功率和抗抖动性上略有提升，但平均规划耗时达17秒，远高于GET-2D-1.0的683毫秒。
原文摘要 · Abstract (English)
In this paper, we introduce GET-2D-1.0, a fast grasp planner for the GET asymmetrical gripper that operates from a single-view RGB-D image, using the Ferrari-Canny metric and a novel sampling strategy, and GET-3D-1.0, a mesh-based method using a 3D gripper model and ray-tracing. We evaluate both grasp planners against baselines with physical experiments, which suggest that GET-2D-1.0 can improve over a bounding box baseline by over 40% in lift success, shake survival, and force resistance. Experiments with GET-3D-1.0 suggest slight improvement compared to GET-2D-1.0 on lift success and shake survival, but are more computationally expensive, averaging 17 seconds of planning compared to 683 ms for GET-2D-1.0.
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