Cloud-based robot grasping with the google object recognition engine

Rapidly expanding internet resources and wireless networking have potential to liberate robots and automation systems from limited onboard computation, memory, and software. “Cloud Robotics” describes an approach that recognizes the wide availability of networking and incorporates open-source elements to greatly extend earlier concepts of “Online Robots” and “Networked Robots”. In this paper we consider how cloud-based data and computation can facilitate 3D robot grasping. We present a system architecture, implemented prototype, and initial experimental data for a cloud-based robot grasping system that incorporates a Willow Garage PR2 robot with onboard color and depth cameras, Google's proprietary object recognition engine, the Point Cloud Library (PCL) for pose estimation, Columbia University's GraspIt! toolkit and OpenRAVE for 3D grasping and our prior approach to sampling-based grasp analysis to address uncertainty in pose. We report data from experiments in recognition (a recall rate of 80% for the objects in our test set), pose estimation (failure rate under 14%), and grasping (failure rate under 23%) and initial results on recall and false positives in larger data sets using confidence measures.

Authors

• Ben Kehoe
• Akihiro Matsukawa
• Sal Candido
• James J. Kuffner
• Ken Goldberg

Keywords

• Robots
• Object recognition
• Three-dimensional displays
• Servers
• Training
• Estimation
• Google
• Cloud Computing
• Robotic Grasping
• False Positive
• Large Datasets
• Large Set
• Point Cloud
• System Architecture
• Depth Camera
• Pose Estimation
• Measure Of Confidence
• Prototype Implementation
• Training Set
• Random Sampling
• Big Data
• 3D Reconstruction
• Image Object
• Semantic Information
• Training Images
• 3D Mesh
• Online Phase
• Image Recognition
• Deodorant
• Triangular Model
• Second-order Moments
• Multiple Objects
• Object Pose
• Single Recognition
• Path Planning