A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass)
Nan Tian, Matthew Matl, Jeffrey Mahler, Yu Xiang Zhou, Samantha Staszak, Christopher Correa, Steven Zheng, Qiang Li
Abstract
In support of Cloud Robotics, Robotics and Automation as a Service (RAaaS) frameworks have the potential to reduce the complexity of software development, simplify software installation and maintenance, and facilitate data sharing for machine learning. In this proof-of-concept paper, we describe Berkeley Robotics and Automation as a Service (Brass), a RAaaS prototype that allows robots to access a remote server that hosts a robust grasp-planning system (Dex-Net 1.0) that maintains data on hundreds of candidate grasps on thousands of 3D object meshes and uses perturbation sampling to estimate and update a stochastic robustness metric for each grasp. Results suggest that such a system can increase grasp reliability over naive locally-computed grasping strategies with network latencies of 30 and 200 msec for servers 500 and 6000 miles away, respectively. We also study how the system can use execution reports from robots in the field to update grasp recommendations over time.
BibTeX
@inproceedings{icra2017_acloudrobotsyste,
title = {A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass)},
author = {Nan Tian and Matthew Matl and Jeffrey Mahler and Yu Xiang Zhou and Samantha Staszak and Christopher Correa and Steven Zheng and Qiang Li and Robert Zhang and Ken Goldberg},
booktitle = {ICRA 2017},
year = {2017}
}