Nonprehensile manipulation, such as pushing and pulling, enables robots to move, align, or reposition objects that may be difficult to grasp due to their geometry, size, or relationship to the robot or the environment. Much of the existing work in nonprehensile manipulation relies on parallel-jaw grippers or tools such as rods and spatulas. In contrast, multi-fingered dexterous hands offer richer contact modes and versatility for handling diverse objects to provide stable support over the objects, which compensates for the difficulty of modeling the dynamics of nonprehensile manipulation. Therefore, we propose Geometry-aware Dexterous Pushing and Pulling(GD2P) for nonprehensile manipulation with dexterous robotic hands. We study pushing and pulling by framing the problem as synthesizing and learning pre-contact dexterous hand poses that lead to effective manipulation. We generate diverse hand poses via contact-guided sampling, filter them using physics simulation, and train a diffusion model conditioned on object geometry to predict viable poses. At test time, we sample hand poses and use standard motion planners to select and execute pushing and pulling actions. We perform extensive real-world experiments with an Allegro Hand and a LEAP Hand, demonstrating that GD2P offers a scalable route for generating dexterous nonprehensile manipulation motions with its applicability to different hand morphologies. Our project website is available at: geodex2p.github.io.

翻译：非抓取操作（如推和拉）能使机器人移动、对齐或重新定位那些因几何形状、尺寸或其与机器人及环境的关系而难以抓取的物体。现有非抓取操作研究多依赖平行爪夹持器或杆、铲等工具。相比之下，多指灵巧手可提供更丰富的接触模式与操作多样性，能以稳定支撑覆盖物体表面，从而弥补非抓取操作动力学建模的困难。为此，我们提出几何感知灵巧推拉方法（GD2P）用于灵巧手非抓取操作。通过将推拉问题重构为合成并学习接触前灵巧手姿态以达成有效操作，我们对此展开研究。利用接触引导采样生成多样化手部姿态，经物理仿真筛选后，训练以物体几何为条件的扩散模型预测可行姿态。测试阶段，采样手部姿态并采用标准运动规划器选择与执行推拉动作。我们使用Allegro手与LEAP手开展了大量真实世界实验，证明GD2P可扩展至不同手部形态，为生成灵巧非抓取操作运动提供可规模化方案。项目网站：geodex2p.github.io。