Introduction
In this research area, we are exploring new deep learning techniques to efficiently train the robot manipulators to allow them to learn and perform complex tasks.
Goals
Development of deep learning algorithms for robot manipulators
Building a deep learning framework for efficient collaboration of human and mobile manipulators in advanced manufacturing environments
Video Demonstrations
Grasping objects from point cloud data
Deep learning for robot manipulator
Publications
- D. Bui, H. Nguyen, H. M. La* and S. Li. A Deep Learning-Based Autonomous Robot Manipulator for Sorting Application. Proceedings of the 4th IEEE International Conference on Robotic Computing (IRC), March 9-11, 2020, Taichung, Taiwan. PDF
- H. Nguyen, and H. M. La. Review of Deep Reinforcement Learning for Robot Manipulation. In Proceedings of the third IEEE International Conference on Robotic Computing (IRC), February 25-27, 2019, Naples, Italy. PDF
- A. Sehgal, H. M. La, S. Louis, and H. Nguyen. Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization. In Proceedings of the third IEEE International Conference on Robotic Computing (IRC), February 25-27, 2019, Naples, Italy. PDF
- H. Nguyen, H. M. La, and M. Deans. Deep Learning with Experience Ranking Convolutional Neural Network for Robot Manipulator. arXiv:1809.05819 [cs.RO], September 2018. Source code: Github.
- L. Jin, S. Li, H. M. La, and X. Luo. Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks. IEEE Transactions on Industrial Electronics. Volume 64, Issue 6, Pages 4710 – 4720, June 2017.
