DexGrasp-Zero：一种形态对齐的策略用于零样本跨躯体灵巧抓取
DexGrasp-Zero: A Morphology-Aligned Policy for Zero-Shot Cross-Embodiment Dexterous Grasping
AI总结：
本文提出DexGrasp-Zero策略，通过形态对齐图表示和MAGCN网络，实现跨躯体灵巧抓取的零样本迁移，实验表明在未见过的硬件上成功率达到85%。
摘要：
为满足日益多样化的灵巧手硬件需求，需开发一种能够实现零样本跨躯体抓取而无需冗余学习的策略。跨躯体对齐因异构手动力学和物理约束而具有挑战性。现有方法通常预测中间运动目标并将其重新定位到每个躯体，这可能引入误差并违反特定躯体的限制，阻碍在不同手之间的转移。为克服这些限制，我们提出了DexGrasp-Zero，一种从不同躯体学习通用抓取技能的策略，实现零样本迁移。我们首先引入一种形态对齐的图表示，将每个手的动力学关键点映射到解剖学基础节点，并为每个节点配备三轴正交运动原语，实现跨不同形态的结构和语义对齐。依赖此图表示，我们设计了形态对齐图卷积网络（MAGCN）以编码图进行策略学习。MAGCN结合了物理属性注入机制，将手特定的物理约束融合到图特征中，实现对不同连接长度和驱动限制的自适应补偿，以实现精确稳定的抓取。我们在YCB数据集上的广泛模拟评估表明，我们的策略在四个异构手（Allegro、Shadow、Schunk、Ability）上联合训练后，在未见过的硬件（LEAP、Inspire）上实现了85%的零样本成功率，优于现有最先进方法59.5%。实际实验进一步在三个机器人平台上（LEAP、Inspire、Revo2）评估我们的策略，在未见过的物体上平均成功率为82%。
英文摘要：
To meet the demands of increasingly diverse dexterous hand hardware, it is crucial to develop a policy that enables zero-shot cross-embodiment grasping without redundant re-learning. Cross-embodiment alignment is challenging due to heterogeneous hand kinematics and physical constraints. Existing approaches typically predict intermediate motion targets and retarget them to each embodiment, which may introduce errors and violate embodiment-specific limits, hindering transfer across diverse hands. To overcome these limitations, we propose DexGrasp-Zero, a policy that learns universal grasping skills from diverse embodiments, enabling zero-shot transfer to unseen hands. We first introduce a morphology-aligned graph representation that maps each hand's kinematic keypoints to anatomically grounded nodes and equips each node with tri-axial orthogonal motion primitives, enabling structural and semantic alignment across different morphologies. Relying on this graph-based representation, we design a Morphology-Aligned Graph Convolutional Network (MAGCN) to encode the graph for policy learning. MAGCN incorporates a Physical Property Injection mechanism that fuses hand-specific physical constraints into the graph features, enabling adaptive compensation for varying link lengths and actuation limits for precise and stable grasping. Our extensive simulation evaluations on the YCB dataset demonstrate that our policy, jointly trained on four heterogeneous hands (Allegro, Shadow, Schunk, Ability), achieves an 85% zero-shot success rate on unseen hardware (LEAP, Inspire), outperforming the state-of-the-art method by 59.5%. Real-world experiments further evaluate our policy on three robot platforms (LEAP, Inspire, Revo2), achieving an 82% average success rate on unseen objects.
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。
