Beyond Top-Grasps Through Scene Completion

Abstract

Current end-to-end grasp planning methods propose grasps in the order of seconds that attain high grasp success rates on a diverse set of objects, but often by constraining the workspace to top-grasps. In this work, we present a method that allows end-to-end top-grasp planning methods to generate full six-degree-of-freedom grasps using a single RGB-D view as input. This is achieved by estimating the complete shape of the object to be grasped, then simulating different viewpoints of the object, passing the simulated viewpoints to an end-to-end grasp generation method, and finally executing the overall best grasp. The method was experimentally validated on a Franka Emika Panda by comparing 429 grasps generated by the state-of-the-art Fully Convolutional Grasp Quality CNN, both on simulated and real camera images. The results show statistically significant improvements in terms of grasp success rate when using simulated images over real camera images, especially when the real camera viewpoint is angled. Code and video are available at https://irobotics.aalto.fi/beyond-top-grasps-through-scene-completion/.

arXiv commentAccepted to 2020 IEEE Conference on Robotics and Automation (ICRA)

References in corpus (5)

• Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
• Robust Grasp Planning Over Uncertain Shape Completions
• Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty
• Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours
• Multi-Modal Geometric Learning for Grasping and Manipulation

Cited by in corpus (7)

• Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps
• Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Clutter
• TransSC: Transformer-based Shape Completion for Grasp Evaluation
• Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivity
• VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction
• DDGC: Generative Deep Dexterous Grasping in Clutter
• PCF-Grasp: Converting Point Completion to Geometry Feature to Enhance 6-DoF Grasp