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In this paper, we present a novel approach towards feasible dynamic graspingby leveraging Gaussian Process Distance Fields (GPDF), SE(3) equivariance, andRiemannian Mixture Models. We seek to improve the grasping capabilities ofrobots in dynamic tasks where objects may be moving. The proposed methodcombines object shape reconstruction, grasp sampling, and grasp pose selectionto enable effective grasping in such scenarios. By utilizing GPDF, the approachaccurately models the shape and physical properties of objects, allowing forprecise grasp planning. SE(3) equivariance ensures that the sampled grasp posesare equivariant to the object's pose. Additionally, Riemannian Gaussian MixtureModels are employed to test reachability, providing a feasible and adaptablegrasping strategy. The sampled feasible grasp poses are used as targets fornovel task or joint space reactive controllers formulated by Gaussian MixtureModels and Gaussian Processes, respectively. Experimental results demonstratethe effectiveness of the proposed approach in generating feasible grasp posesand successful grasping in dynamic environments. (Video:https://www.youtube.com/watch?v=wjIVrwTzTOc&t=70s)

Towards Feasible Dynamic Grasping: Leveraging Gaussian Process Distance Field, SE(3) Equivariance and Riemannian Mixture Models