MachaGrasp：基于形态的跨躯体灵巧手关节生成方法用于抓取
MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping
AI总结：
MachaGrasp提出一种基于形态的跨躯体灵巧手抓取生成方法，通过形态嵌入和特征抓取集生成抓取动作，实现高成功率的抓取任务。
摘要：
多指手的灵巧抓取仍然具有挑战性，因为高维关节和基于优化的管道成本较高。现有的端到端方法需要在大规模数据集上进行训练以适应特定的手，限制了其在不同躯体上的泛化能力。我们提出了MachaGrasp，一种基于特征抓取的端到端框架，用于跨躯体抓取生成。从手的形态描述中，我们推导出形态嵌入和特征抓取集。在这些基础上，结合物体点云和腕部姿态，幅度预测器在低维空间中回归关节系数，这些系数被解码为完整的关节关节。关节学习通过一种运动感知的关节损失（KAL）进行监督，强调指尖相关的运动并注入形态特定的结构。在未见过的物体上跨三个灵巧手的模拟中，MachaGrasp在每种抓取中获得不到0.4秒的推理时间，平均抓取成功率达到91.9%。通过少量适应到未见过的手，它在模拟中对未见过的物体达到85.6%的成功率，在现实世界实验中对这种少量泛化手达到87%的成功率。代码和额外材料可在我们的项目网站上获得https://connor-zh.github.io/MachaGrasp。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
