学习几何感知的非抓取推拉与拉拽技术：灵活手部应用
Learning Geometry-Aware Nonprehensile Pushing and Pulling with Dexterous Hands
AI总结：
本文提出GD2P方法，通过生成和学习预接触手部姿态实现非抓取推拉，利用接触引导采样和物理模拟生成有效姿态，展示灵活手部在非抓取操作中的应用潜力。
摘要：
非抓取操作，如推和拉，使机器人能够移动、对齐或重新定位可能因几何、大小或与机器人或环境关系难以抓取的对象。现有非抓取操作研究大多依赖平行牙抓取器或工具如杆和 spatulas。相比之下，多指灵活手提供更丰富的接触模式和处理多样化物体的能力，以提供稳定的支撑，弥补非抓取操作动力学建模的难度。因此，我们提出几何感知的灵活推拉（GD2P）方法用于具有灵活机械手的非抓取操作。我们通过将问题框架为合成和学习预接触灵活手姿态来研究推拉。我们通过接触引导采样生成多样化的手部姿态，通过物理模拟过滤它们，并训练一个条件于物体几何的扩散模型来预测可行姿态。在测试时，我们采样手部姿态并使用标准运动规划器选择和执行推拉动作。我们进行了大量实际世界实验，使用Allegro手和LEAP手，证明GD2P在不同手形中生成灵活非抓取操作动作的可扩展性。我们的项目网站可用：geodex2p.github.io。
英文摘要：
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.
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。
