GaussianGrasper: 3D Language Gaussian Splatting for Open-Vocabulary Robotic Grasping

Constructing a 3D scene capable of accommodating open-ended language queries, is a pivotal pursuit in the domain of robotics, which facilitates robots in executing object manipulations based on human language directives. To achieve this, some research efforts have been dedicated to the development of language-embedded implicit fields. However, implicit fields (e.g. NeRF) encounter limitations due to the necessity of taking images from a larger number of viewpoints for reconstruction, coupled with their inherent inefficiencies in inference. Furthermore, these methods directly distill patch-level 2D features, leading to ambiguous segmentation boundaries. Thus, we present the GaussianGrasper, which uses 3D Gaussian Splatting (3DGS) to explicitly represent the scene as a set of Gaussian primitives and is capable of real-time rendering. Our approach takes RGB-D images from limited viewpoints as input and uses an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently distill 2D language embeddings and constraint consistency of feature embeddings. With the reconstructed geometry of the Gaussian field, our method enables the pre-trained grasping model to generate collision-free grasp pose candidates. Furthermore, we propose a normal-guided grasp module to select the best grasp pose. Through comprehensive real-world experiments, we demonstrate that GaussianGrasper enables robots to accurately locate and grasp objects according to language instructions, providing a new solution for language-guided grasping tasks.