6-DoF Contrastive Grasp Proposal Network
This addresses a key challenge in robot manipulation by enabling efficient grasp planning with limited sensor data, though it appears incremental in combining existing techniques like region proposal networks with contrastive learning.
The paper tackles the problem of proposing 6-DoF grasp poses for novel objects from single-view depth images, achieving collision-free grasps within 0.5 seconds as demonstrated in physical robot experiments.
Proposing grasp poses for novel objects is an essential component for any robot manipulation task. Planning six degrees of freedom (DoF) grasps with a single camera, however, is challenging due to the complex object shape, incomplete object information, and sensor noise. In this paper, we present a 6-DoF contrastive grasp proposal network (CGPN) to infer 6-DoF grasps from a single-view depth image. First, an image encoder is used to extract the feature map from the input depth image, after which 3-DoF grasp regions are proposed from the feature map with a rotated region proposal network. Feature vectors that within the proposed grasp regions are then extracted and refined to 6-DoF grasps. The proposed model is trained offline with synthetic grasp data. To improve the robustness in reality and bridge the simulation-to-real gap, we further introduce a contrastive learning module and variant image processing techniques during the training. CGPN can locate collision-free grasps of an object using a single-view depth image within 0.5 seconds. Experiments on a physical robot further demonstrate the effectiveness of the algorithm.