3 accepted papers
Achieving dexterous grasping remains a key challenge in robotics. Recent generative approaches enable diverse grasps through large-scale data-driven training, yet they often neglect geometric priors of objects, which leads to low data efficiency and poor physical plausibility. We propose GeoDexGrasp
Constructing a unified canonical pose representation for 3D object categories is crucial for pose estimation and robotic scene understanding. Previous unified pose representations often relied on manual alignment, such as in ShapeNet and ModelNet. Recently, self-supervised canonicalization methods h
Anomaly detection is a task of identifying samples that significantly differ from the majority. However, most typical anomaly detection methods often prioritize accuracy over interpretability. To address this limitation, we propose an explainable anomaly detection approach using a deep learnable M-b…