The OOD score is then determined by combining the deviation from the input data to the ID pattern in both subspaces.
We propose a new 6-DoF grasp pose synthesis approach from 2D/2. 5D input based on keypoints.
Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern.
Unfortunately, the top performing affordance recognition methods use object category priors to boost the accuracy of affordance detection and segmentation.
By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot.