  Constructing a 3D scene capable of accommodating open-ended language queries,
is a pivotal pursuit, particularly within the domain of robotics. Such
technology facilitates robots in executing object manipulations based on human
language directives. To tackle this challenge, 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
processing a large number of input views for reconstruction, coupled with their
inherent inefficiencies in inference. Thus, we present the GaussianGrasper,
which utilizes 3D Gaussian Splatting to explicitly represent the scene as a
collection of Gaussian primitives. Our approach takes a limited set of RGB-D
views and employs a tile-based splatting technique to create a feature field.
In particular, we propose an Efficient Feature Distillation (EFD) module that
employs contrastive learning to efficiently and accurately distill language
embeddings derived from foundational models. 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 query and grasp objects with language instructions, providing a new
solution for language-guided manipulation tasks. Data and codes can be
available at https://github.com/MrSecant/GaussianGrasper.
