BiNoMaP: 学习类别级双臂非抓取操作原语
BiNoMaP: Learning Category-Level Bimanual Non-Prehensile Manipulation Primitives
AI总结：
BiNoMaP提出了一种无强化学习的双臂非抓取操作原语学习框架，通过几何感知优化和参数化方法实现类别级泛化和跨平台迁移。
摘要：
非抓取操作，包括无法抓取的动作如推动、戳击、旋转和包裹，由于其接触丰富且分析上难以处理的性质，仍然处于研究的边缘。我们从两个视角重新审视这个问题。首先，而不是依赖单臂设置或有利的环境支持（例如墙壁或边缘），我们倡导一种通用的双臂配置，并建立一套双臂非抓取操作原语（BiNoMaP）。其次，脱离现有的基于强化学习的方法，我们提出一个三阶段、无强化学习框架，用于学习结构化的非抓取技能。我们首先从视频演示中提取双臂手部运动轨迹。由于这些粗略轨迹受到感知噪声和形态学差异的影响，我们引入一种几何意识的后优化算法，将它们细化为与预定义运动模式一致的可执行操作原语。为了实现类别级泛化，学习到的原语进一步通过与对象相关的几何属性进行参数化，主要为大小，从而适应具有显著形状变化的未见过的实例。重要的是，BiNoMaP支持跨躯体转移：相同的原语可以在具有不同运动学配置的两个真实双臂平台上部署，而无需重新设计技能结构。在多样化的物体和空间配置上的大量真实机器人实验展示了我们方法的有效性、效率和强大的泛化能力。
英文摘要：
Non-prehensile manipulation, encompassing ungraspable actions such as pushing, poking, pivoting, and wrapping, remains underexplored due to its contact-rich and analytically intractable nature. We revisit this problem from two perspectives. First, instead of relying on single-arm setups or favorable environmental supports (e.g., walls or edges), we advocate a generalizable dual-arm configuration and establish a suite of Bimanual Non-prehensile Manipulation Primitives (BiNoMaP). Second, departing from prevailing RL-based approaches, we propose a three-stage, RL-free framework for learning structured non-prehensile skills. We begin by extracting bimanual hand motion trajectories from egocentric video demonstrations. Since these coarse trajectories suffer from perceptual noise and morphological discrepancies, we introduce a geometry-aware post-optimization algorithm to refine them into executable manipulation primitives consistent with predefined motion patterns. To enable category-level generalization, the learned primitives are further parameterized by object-relevant geometric attributes, primarily size, allowing adaptation to unseen instances with significant shape variations. Importantly, BiNoMaP supports cross-embodiment transfer: the same primitives can be deployed on two real-world dual-arm platforms with distinct kinematic configurations, without redesigning skill structures. Extensive real-robot experiments across diverse objects and spatial configurations demonstrate the effectiveness, efficiency, and strong generalization capability of our approach.
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。
