This paper introduces a novel approach to improve robotic grasping in dynamic environments by integrating Gaussian Process Distance Fields (GPDF), SE(3) equivariant networks, and Riemannian Mixture Models. The aim is to enable robots to grasp moving objects effectively. Our approach comprises three main components: object shape reconstruction, grasp sampling, and implicit grasp pose selection. GPDF accurately models the shape of objects, which is essential for precise grasp planning. SE(3) equivariance ensures that the sampled grasp poses are equivariant to the object's pose changes, enhancing robustness in dynamic scenarios. Riemannian Gaussian Mixture Models are employed to assess reachability, providing a feasible and adaptable grasping strategies. Feasible grasp poses are targeted by novel task or joint space reactive controllers formulated using Gaussian Mixture Models and Gaussian Processes. This method resolves the challenge of discrete grasp pose selection, enabling smoother grasping execution. Experimental validation confirms the effectiveness of our approach in generating feasible grasp poses and achieving successful grasps in dynamic environments. By integrating these advanced techniques, we present a promising solution for enhancing robotic grasping capabilities in real-world scenarios.

翻译：摘要：本文提出一种新颖方法，通过集成高斯过程距离场（GPDF）、SE(3)等变网络和黎曼混合模型，提升机器人在动态环境中的抓取能力，旨在使机器人能够有效抓取移动物体。该方法包含三个核心组成部分：物体形状重建、抓取采样和隐式抓取姿态选择。GPDF精确建模物体形状，为精准抓取规划奠定基础；SE(3)等变性确保采样抓取姿态对物体姿态变化具有等变性，增强动态场景下的鲁棒性；采用黎曼高斯混合模型评估可达性，提供可行且自适应的抓取策略。通过基于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控制器，目标可实现的抓取姿态得以实现。该方法解除了离散抓取姿态选择的限制，使抓取执行更加平滑。实验验证表明，该方法在动态环境中生成可行抓取姿态并成功完成抓取任务方面效果显著。通过融合这些先进技术，我们为提升真实场景中机器人抓取能力提供了一种有前景的解决方案。