AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
A reinforcement learning-based framework for robotic grasp refinement, integrating keypoint-based object representations with a Deep Q-Network (DQN), is proposed, offering a scalable and adaptable solution for contact-rich manipulation tasks.
A deep learning-based grasp estimation model designed to enable robotic manipulation with articulated objects that incorporates the attention-based semantic and geometric feature fusion (ASGF) module improved the grasp success rate in the evaluated setting.
Antipodal grasping from single-view red-green-blue and depth (RGB-D) images is challenged by occlusion and partial observability, making purely analytical inference ill-posed. We present the Grasping Framework with Learning-Based Perception and Analytical Modeling (GFLA), which fuses learning-based perception with anal...
Experiments show that M-VTOP achieves sub-millimeter accuracy under complex geometries, occlusions, and tight tolerances, demonstrating its promise for high-precision robotic manipulation.
A novel 7-DoF grasping pose generation framework that integrates sparse attention and null convolution is introduced, which enhances the model’s ability to capture fine-grained features from point clouds, significantly improving the accuracy of parallel gripping pose estimation.
WaveletkAN is a pose-refinement model based on RGB-D point clouds proposed in this paper to correct object pose hypotheses for dense bins with occlusion, specular depth noise, and self-similar industrial parts and can achieve robust and deployable pose correction for RGB-D robotic bin-picking systems.
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