外观
论文arXiv 2609.124981 个月前
ArtManip：类别级铰接物体手内操作
原文链接：ArtManip: Category-Level Articulated In-Hand Manipulation（arXiv 2609.12498 · 发布日期：2026-09-11）
ArtManip: Category-Level Articulated In-Hand Manipulation
ArtManip面向类别级铰接物体灵巧手内操作，结合自动构型生成与两阶段策略训练，在仿真和真实物体上验证泛化。
核心要点
- 动机是铰接物体内部自由度控制与抓取稳定强耦合，且多样模型和功能抓取获取成本高。
- 方法自动程序化生成多样铰接物体，并合成面向任务的功能性初始抓取。
- 策略学习采用两阶段训练，结合关节物理随机化、奖励课程和潜表示蒸馏。
- 实验显示策略在仿真未见实例和多样构型上泛化，并可零样本迁移到真实物体。
验证范围与局限：验证范围包括仿真中的四个物体类别，以及12个形状和关节机制多样的真实物体。
英文摘要 (Abstract)
Category-level in-hand manipulation of articulated objects is a formidable yet underexplored challenge for dexterous robotic hands. This difficulty stems from two core bottlenecks: first, controlling an object's internal degrees of freedom is tightly coupled with maintaining grasp stability on a free-floating base; second, acquiring diverse object models and functional grasps at scale is highly labor-intensive, yet vital for generalization given the system's sensitivity to initial configurations. In this work, we present ArtManip, the first category-level articulated in-hand manipulation method that generalizes across object instances and diverse initial grasps. For initial configuration construction, we develop an automated pipeline that procedurally generates diverse articulated objects and synthesizes task-oriented functional grasps. For policy learning, we propose a robust two-stage training strategy that incorporates articulation physics randomization, reward curriculum, and latent representation distillation to handle complex contact and joint dynamics during deployment. Extensive experiments across four object categories demonstrate that our policy generalizes to unseen instances and varied configurations in simulation, and achieves zero-shot transfer to 12 real-world objects featuring diverse shapes and joint mechanics.
作者：Yang Yang, Tengyu Liu, Puhao Li, Zeyuan Chen, Yuyang Li, Xingwan Wang, Yingying Wu, Zhaopeng Cui, Siyuan Huang
来源
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