Haochong Chen

Generating reliable grasp configurations in cluttered scenes is an important guarantee for robots to successfully grasp objects. Existing learning-based methods encode the voxelized scene into a latent space to predict graspable points, which overlook both the reliability of grasp selection and the explicit geometric constraint imposed by the object’s surface occupancy. In this article, we propose a 3-D sparse grasp network (3-D SPGNet), which maps voxel features of object surfaces to grasp candidates to enforce explicit constraints, while leveraging the dense truncated signed distance function (TSDF) reconstruction to enhance grasp reliability through implicit synergy. Specifically, we first use a 3-D convolutional neural network to extract multiscale features at three different resolutions from the voxelized input. Then, we apply the proposed 3-D surface constraint block to aggregate features and perform explicit geometric mapping on the encoded nonempty voxels. Finally, we reconstruct the scene from sparse to dense and generate the grasp configuration accordingly. Moreover, we propose a novel loss function for estimating reliable grasp scores and poses. Simulation experiments demonstrate that our method improves the grasp success rates and declutter rates by approximately 9% compared to state-of-the-art baselines. In addition, we deploy the model on a real robot arm, and the real-world experimental results show that our method achieves over a 10% improvement in performance, which further verifies the effectiveness of the proposed method.