CoToGrasp：基于接触拓扑条件与规范工作空间学习的灵巧抓取合成方法
CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning
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中文总结 AI 辅助
针对现有灵巧抓取规划器的局限，提出CoToGrasp框架，通过规范工作空间学习实现接触拓扑条件下的灵巧抓取合成，在DexGraspNet数据集上达到最优性能，且可在物理机器人平台验证可行性。
中文摘要 AI 辅助
当前的灵巧抓取规划器主要针对物理稳定性进行优化，关注的是能否抓取物体，而非如何抓取以支持下游功能任务。然而，基于特定人类抓取分类法对抓取合成进行条件约束，通常需要成本过高的带物体标注数据集。为解决这些局限，我们提出CoToGrasp，一种新颖的生成框架，可严格基于特定接触拓扑合成多样且稳定的抓取。为绕开数据收集瓶颈，CoToGrasp完全以物体无关的方式进行训练。我们引入一种基于特征的规范工作空间，将物体局部特征投影到统一的夹爪中心域，有效将语义功能意图与任意物体几何解耦。通过在该工作空间中学习夹爪的内在接触流形，我们的模型在推理时实现了对未见物体的零样本泛化。在大规模DexGraspNet数据集上的大量评估表明，CoToGrasp达到了当前最优性能，优于现有的分类法引导规划器。最后，我们在物理机器人平台上验证了所合成接触拓扑的物理可行性与运动学可行性。代码可在我们的项目网站获取：this https URL。
英文摘要
Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it should be grasped to support downstream functional tasks. However, conditioning grasp synthesis on specific human grasp taxonomies typically requires prohibitively expensive, object-annotated datasets. To address these limitations, we propose CoToGrasp, a novel generative framework that synthesizes diverse, stable grasps strictly conditioned on specific contact topologies. To bypass the data collection bottleneck, CoToGrasp is trained entirely in an object-agnostic manner. We introduce a feature-based canonical workspace that projects local object features into a unified gripper-centric domain, effectively decoupling the semantic functional intent from the arbitrary object geometry. By learning the intrinsic contact manifold of the gripper within this workspace, our model achieves zero-shot generalization to unseen objects at inference. Extensive evaluations on the large-scale DexGraspNet dataset demonstrate that CoToGrasp achieves state-of-the-art performance, outperforming existing taxonomy-guided planners. Finally, we demonstrate the physical viability and kinematic feasibility of our synthesized contact topologies on a physical robot platform. Code is available on our project website at https://cea-list.github.io/cotograspweb/ .
发表机构
- Université Paris-Saclay（巴黎萨克雷大学）
- CEA（法国原子能和替代能源委员会）
- Ecole Centrale Lyon（里昂中央理工学院）
- Institut Universitaire de France (IUF)（法国大学研究院）
机构由 AI 辅助整理，请以论文原文为准。
