用几何感知方法让灵巧手学会推拉物体，无需抓取。
Learning Geometry-Aware Nonprehensile Pushing and Pulling with Dexterous Hands
- 通过物理仿真筛选接触引导的灵巧手姿态，生成有效操作姿势。
- 在Allegro和LEAP手实测中实现稳定推拉，适配不同手型。
- 基于扩散模型预测可行姿态，适合复杂形状物体操作。
非捏握操作（如推拉）使机器人能移动、对齐或重新定位因几何、尺寸或与环境关系而难以抓取的物体。现有工作多依赖平行钳爪或杆、刮刀等工具，而多指灵巧手具备更丰富的接触模式和适应性，可为多样物体提供稳定支撑，弥补非捏握操作动力学建模的困难。为此，我们提出几何感知灵巧推拉（GD2P）方法，将推拉问题转化为合成与学习有效操作前的手部姿态。通过接触引导采样生成多样化手姿，经物理仿真过滤后，训练基于物体几何的扩散模型以预测可行姿态。测试时，采样手姿并使用标准运动规划器选择并执行推拉动作。我们在Allegro手和LEAP手进行大量真实实验，验证了GD2P在不同手型下的可扩展性与有效性。项目主页：geodex2p.github.io。
原文摘要 · Abstract (English)
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.
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