Learning 6-DOF Grasping Interaction with Deep Geometry-aware 3D Representations

by   Xinchen Yan, et al.

This paper focuses on the problem of learning 6-DOF grasping with a parallel jaw gripper in simulation. We propose the notion of a geometry-aware representation in grasping based on the assumption that knowledge of 3D geometry is at the heart of interaction. Our key idea is constraining and regularizing grasping interaction learning through 3D geometry prediction. Specifically, we formulate the learning of deep geometry-aware grasping model in two steps: First, we learn to build mental geometry-aware representation by reconstructing the scene (i.e., 3D occupancy grid) from RGBD input via generative 3D shape modeling. Second, we learn to predict grasping outcome with its internal geometry-aware representation. The learned outcome prediction model is used to sequentially propose grasping solutions via analysis-by-synthesis optimization. Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. This dataset includes 101 everyday objects spread across 7 categories, additionally, we propose a data augmentation strategy for effective learning; (3) We demonstrate that the learned geometry-aware representation leads to about 10 percent relative performance improvement over the baseline CNN on grasping objects from our dataset. (4) We further demonstrate that the model generalizes to novel viewpoints and object instances.


page 2

page 6

page 7

page 9


Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks

Training a deep network policy for robot manipulation is notoriously cos...

Learning Robust Real-World Dexterous Grasping Policies via Implicit Shape Augmentation

Dexterous robotic hands have the capability to interact with a wide vari...

AdaGrasp: Learning an Adaptive Gripper-Aware Grasping Policy

This paper aims to improve robots' versatility and adaptability by allow...

Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interaction

We approach the problem of high-DOF reaching-and-grasping via learning j...

Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies

Grasping objects of different shapes and sizes - a foundational, effortl...

An Integrated Simulator and Dataset that Combines Grasping and Vision for Deep Learning

Deep learning is an established framework for learning hierarchical data...

Please sign up or login with your details

Forgot password? Click here to reset