Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp Sampling Loss
Xinghao Zhu,Yefan Zhou,Yongxiang Fan,Lingfeng Sun,Jianyu Chen,Masayoshi Tomizuka,Xinghao Zhu,Yefan Zhou,Yongxiang Fan,Lingfeng Sun,Jianyu Chen,Masayoshi Tomizuka
Robotic grasping for a diverse set of objects is essential in many robot manipulation tasks. One promising approach is to learn deep grasping models from large training datasets of object images and grasp labels. However, empirical grasping datasets are typically sparsely labeled (i.e., a small number of successful grasp labels**Labels refer to marking the image to indicate a successful robotic gr...
