用扩散模型从部分点云生成抓取姿势，成功率超90%。
Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3
- 在旋转与平移的流形空间中设计扩散过程，生成多样抓取姿势。
- 引入碰撞规避引导，使真实场景下抓取成功率达90%。
- 结合加速技术，实现快速推理，适合实时机器人应用。
从单视角相机成功抓取物体是机器人操作中的关键挑战。现有方法依赖仿真生成大量物体与抓取姿势对，再训练条件生成模型以部署时快速响应。但抓取姿势高度多模态，即同一物体有多种可行抓法。为此，本文采用扩散模型，基于物体的部分点云采样候选抓取姿势。创新点在于将扩散过程定义于SO(3)×R³的流形空间，并引入碰撞规避成本引导，提升推理阶段的抓取成功率。为加速采样，利用扩散模型最新技术实现更快推理。实验表明，该方法在仿真和真实场景中均能以90%成功率抓取多个物体，性能优于多个基线方法。
原文摘要 · Abstract (English)
Grasping objects successfully from a single-view camera is crucial in many robot manipulation tasks. An approach to solve this problem is to leverage simulation to create large datasets of pairs of objects and grasp poses, and then learn a conditional generative model that can be prompted quickly during deployment. However, the grasp pose data is highly multimodal since there are several ways to grasp an object. Hence, in this work, we learn a grasp generative model with diffusion models to sample candidate grasp poses given a partial point cloud of an object. A novel aspect of our method is to consider diffusion in the manifold space of rotations and to propose a collision-avoidance cost guidance to improve the grasp success rate during inference. To accelerate grasp sampling we use recent techniques from the diffusion literature to achieve faster inference times. We show in simulation and real-world experiments that our approach can grasp several objects from raw depth images with $90\%$ success rate and benchmark it against several baselines.
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