跨体型灵巧手抓取生成新框架，无需大量训练数据即可适配不同手型。
MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping
- 基于形态嵌入和特征抓取集，端到端生成低维关节参数
- 仿真中对未知物体抓取成功率91.9%，单次推理耗时<0.4秒
- 少量样本可适配新手型，真实场景仍达87%成功率
灵巧手的高维关节运动与优化管道的高成本使其抓取任务极具挑战。现有端到端方法需针对特定手型在大规模数据上训练，难以跨体型泛化。本文提出MachaGrasp，一种基于特征抓取的端到端跨体型抓取生成框架。从手部形态描述中提取形态嵌入与特征抓取集，结合物体点云和腕部姿态，由幅度预测器回归低维关节系数，并解码为完整关节动作。通过强调指尖相关运动并注入形态特异性结构的运动学感知损失（KAL）进行监督。在三个灵巧手的未见物体上仿真测试，平均抓取成功率达91.9%，单次推理时间低于0.4秒。在少样本适配新手型后，仿真中对未见物体成功率达85.6%，真实实验中达到87%。代码与补充材料见项目主页。
原文摘要 · Abstract (English)
Dexterous grasping with multi-fingered hands remains challenging due to high-dimensional articulations and the cost of optimization-based pipelines. Existing end-to-end methods require training on large-scale datasets for specific hands, limiting their ability to generalize across different embodiments. We propose MachaGrasp, an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation. From a hand's morphology description, we derive a morphology embedding and an eigengrasp set. Conditioned on these, together with the object point cloud and wrist pose, an amplitude predictor regresses articulation coefficients in a low-dimensional space, which are decoded into full joint articulations. Articulation learning is supervised with a Kinematic-Aware Articulation Loss (KAL) that emphasizes fingertip-relevant motions and injects morphology-specific structure. In simulation on unseen objects across three dexterous hands, MachaGrasp attains a 91.9% average grasp success rate with less than 0.4 seconds inference per grasp. With few-shot adaptation to an unseen hand, it achieves 85.6% success on unseen objects in simulation, and real-world experiments on this few-shot-generalized hand achieve an 87% success rate. The code and additional materials are available on our project website https://connor-zh.github.io/MachaGrasp.
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