GraspSplats: Efficient Manipulation with 3D Feature Splatting

• URL: http://arxiv.org/abs/2409.02084v1
• Date: Tue, 3 Sep 2024 17:35:48 GMT
• Title: GraspSplats: Efficient Manipulation with 3D Feature Splatting
• Authors: Mazeyu Ji, Ri-Zhao Qiu, Xueyan Zou, Xiaolong Wang,
• Abstract summary: We present GraspSplats, which generates high-quality scene representations in under 60 seconds. With extensive experiments on a Franka robot, we demonstrate that GraspSplats significantly outperforms existing methods.
• Score: 13.654484429008964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability for robots to perform efficient and zero-shot grasping of object parts is crucial for practical applications and is becoming prevalent with recent advances in Vision-Language Models (VLMs). To bridge the 2D-to-3D gap for representations to support such a capability, existing methods rely on neural fields (NeRFs) via differentiable rendering or point-based projection methods. However, we demonstrate that NeRFs are inappropriate for scene changes due to their implicitness and point-based methods are inaccurate for part localization without rendering-based optimization. To amend these issues, we propose GraspSplats. Using depth supervision and a novel reference feature computation method, GraspSplats generates high-quality scene representations in under 60 seconds. We further validate the advantages of Gaussian-based representation by showing that the explicit and optimized geometry in GraspSplats is sufficient to natively support (1) real-time grasp sampling and (2) dynamic and articulated object manipulation with point trackers. With extensive experiments on a Franka robot, we demonstrate that GraspSplats significantly outperforms existing methods under diverse task settings. In particular, GraspSplats outperforms NeRF-based methods like F3RM and LERF-TOGO, and 2D detection methods.

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