OpenDexGrasp：开放词汇的任务导向灵巧抓取

中文摘要 AI 辅助

灵巧抓取合成在生成稳定且物理上合理的手部姿态方面取得了快速进展，但真实世界的操作要求抓取能够保持任务所隐含的功能。我们研究开放词汇的任务导向灵巧抓取生成，其中机器人必须从自由形式的语言中推断功能意图，将其基于多视角视觉观察和物体几何进行落地，并生成可执行的高自由度抓取。我们提出OpenDexGrasp，一个用于该场景的统一数据和生成建模框架。OpenDexVerse提供了由覆盖到对齐（C2A）配方组织的双源监督：OpenDex-Scale通过自动抓取合成和视觉-语言标注提供大规模语义和几何覆盖，而OpenDex-Align通过人类遥操作和类别级迁移提供高质量的具身对齐。OpenDexGrasp学习一个共享的感知-行动潜在表示，将开放词汇的视觉-语言上下文与灵巧动作生成耦合。可供性落地和抓取生成在该潜在空间上提供互补监督，使得无需单独的可供性到姿态推理阶段即可直接生成任务一致的灵巧抓取。广泛的仿真和真实机器人实验证明了功能对齐、物理可行性、对未见类别的泛化能力以及真实世界执行成功率的提升。更多细节和视频可在该https URL获取。

英文摘要

Dexterous grasp synthesis has advanced rapidly in generating stable and physically plausible hand poses, but real-world manipulation requires grasps that preserve the function implied by the task. We study open-vocabulary task-oriented dexterous grasp generation, where a robot must infer functional intent from free-form language, ground it in multi-view visual observations and object geometry, and generate an executable high-degree-of-freedom grasp. We present OpenDexGrasp, a unified data and generative modeling framework for this setting. OpenDexVerse provides dual-source supervision organized by the Coverage-to-Alignment (C2A) Recipe: OpenDex-Scale offers large-scale semantic and geometric coverage through automatic grasp synthesis and vision-language annotation, while OpenDex-Align supplies high-quality embodied alignment through human teleoperation and category-level transfer. OpenDexGrasp learns a shared perception-action latent representation that couples open-vocabulary vision-language context with dexterous action generation. Affordance grounding and grasp generation provide complementary supervision over this latent space, enabling direct generation of task-consistent dexterous grasps without a separate affordance-to-pose inference stage. Extensive simulation and real-robot experiments demonstrate improved functional alignment, physical feasibility, generalization to unseen categories, and real-world execution success. Additional details and videos are available at https://opendexgrasp.github.io/.

发表机构

• PrimeBot

机构由 AI 辅助整理，请以论文原文为准。

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Accepted at CoRL2026