In this paper, we present a novel approach towards feasible dynamic grasping by leveraging Gaussian Process Distance Fields (GPDF), SE(3) equivariance, and Riemannian Mixture Models. We seek to improve the grasping capabilities of robots in dynamic tasks where objects may be moving. The proposed method combines object shape reconstruction, grasp sampling, and grasp pose selection to enable effective grasping in such scenarios. By utilizing GPDF, the approach accurately models the shape and physical properties of objects, allowing for precise grasp planning. SE(3) equivariance ensures that the sampled grasp poses are equivariant to the object's pose. Additionally, Riemannian Gaussian Mixture Models are employed to test reachability, providing a feasible and adaptable grasping strategy. The sampled feasible grasp poses are used as targets for novel task or joint space reactive controllers formulated by Gaussian Mixture Models and Gaussian Processes, respectively. Experimental results demonstrate the effectiveness of the proposed approach in generating feasible grasp poses and successful grasping in dynamic environments.

翻译：本文提出一种新颖的可行动态抓取方法，通过结合高斯过程距离场（GPDF）、SE(3)等变性和黎曼混合模型。我们旨在提升机器人在物体可能移动的动态任务中的抓取能力。该方法集成了物体形状重建、抓取采样和抓取位姿选择，以实现此类场景下的有效抓取。通过利用GPDF，该方法精准建模物体的形状与物理属性，从而支持精确的抓取规划。SE(3)等变性确保采样得到的抓取位姿与物体位姿具有等变关系。此外，采用黎曼高斯混合模型测试可达性，提供一种可行且自适应的抓取策略。采样得到的可行抓取位姿被用作目标，分别输入由高斯混合模型和高斯过程构建的新型任务空间或关节空间反应式控制器。实验结果证明了所提方法在生成可行抓取位姿及动态环境中成功抓取方面的有效性。