Fangyi Zhang

Fangyi Zhang
Queensland University of Technology | QUT · Faculty of Science and Engineering

PhD in Robotics

About

16
Publications
6,935
Reads
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491
Citations
Introduction
Fangyi Zhang currently works at the Australian Centre for Robotic Vision, Queensland University of Technology. Fangyi does research in Robot Learning, Robotic Vision, Sim-to-real Transfer, Reinforcement Learning, Deep Learning, Robotic Manipulation, and Autonomous Systems. Their current project is 'Vision-Based Robotic Manipulation Learning through Exploration.'
Additional affiliations
March 2014 - December 2014
The Hong Kong University of Science and Technology
Position
  • Research Assistant
Description
  • Did Robotics relevant research, e.g., 3D sensing and VLC-based localization.
Education
February 2015 - June 2018
Queensland University of Technology
Field of study
  • Robotics and Autonomous Systems, EECS
September 2006 - June 2010
East China JiaoTong University
Field of study
  • Automation, Electrical and Electronic Engineering

Publications

Publications (16)
Article
Full-text available
Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the transfer, most existing approaches need real-world images with labels. However, the labeling process is often expensive or even impractical in many robotic...
Preprint
Full-text available
Various approaches have been proposed to learn visuo-motor policies for real-world robotic applications. One solution is first learning in simulation then transferring to the real world. In the transfer, most existing approaches need real-world images with labels. However, the labelling process is often expensive or even impractical in many robotic...
Conference Paper
Full-text available
While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly. To increase the practicality of these techniques on real robots, we propose a modular deep reinforcement learning method capable of transferring models train...
Article
Full-text available
A modular method is proposed to learn and transfer visuo-motor policies from simulation to the real world in an efficient manner by combining domain randomization and adaptation. The feasibility of the approach is demonstrated in a table-top object reaching task where a 7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter thro...
Conference Paper
Full-text available
A modular method is proposed to learn and transfer visuo-motor policies from simulation to the real world in an efficient manner by combining domain randomization and adaptation. The feasibility of the approach is demonstrated in a table-top object reaching task where a 7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter thro...
Article
Full-text available
This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently. Benefiting from weighted losses, the fine-tuning method significantly improves the performance of the policies for a robotic planar reaching task.
Article
We propose to use visible-light beacons for low-cost indoor localization. Modulated light-emitting diode (LED) lights are adapted for localization as well as illumination. The proposed solution consists of two components: light-signal decomposition and Bayesian localization.
Article
Full-text available
In this paper we describe a deep network architecture that maps visual input to control actions for a robotic planar reaching task with 100% reliability in real-world trials. Our network is trained in simulation and fine-tuned with a limited number of real-world images. The policy search is guided by a kinematics-based controller (K-GPS), which wor...
Article
Full-text available
Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress as they make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of co...
Article
Full-text available
This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and...
Conference Paper
For mobile robots and position-based services, localization is the most fundamental capability while path-planning is an important application based on that. A novel localization and path-planning solution based on a low-cost Visible Light Communication (VLC) system for indoor environments is proposed in this paper. A number of modulated LED lights...
Conference Paper
Indoor localization is a fundamental capability for service robots and indoor applications on mobile devices. To realize that, the cost and performance are of great concern. In this paper, we introduce a lightweight signal encoding and decomposition method for a low-cost and low-power Visible Light Communication (VLC)-based indoor localization syst...

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