Next Article in Journal
Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization
Previous Article in Journal
Tie-System Calibration for the Experimental Setup of Large Deployable Reflectors
Open AccessArticle

Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot

1
Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USA
2
Mitsubishi Electric Research Laboratories, Cambridge, MA 02139, USA
3
Department of Electrical Engineering, Pennsylvania State University, University Park, PA 16802, USA
*
Author to whom correspondence should be addressed.
Machines 2019, 7(2), 24; https://doi.org/10.3390/machines7020024
Received: 27 January 2019 / Revised: 10 April 2019 / Accepted: 10 April 2019 / Published: 15 April 2019
This paper presents and experimentally validates a concept of end-to-end imitation learning for autonomous systems by using a composite architecture of convolutional neural network (ConvNet) and Long Short Term Memory (LSTM) neural network. In particular, a spatio-temporal deep neural network is developed, which learns to imitate the policy used by a human supervisor to drive a car-like robot in a maze environment. The spatial and temporal components of the imitation model are learned by using deep convolutional network and recurrent neural network architectures, respectively. The imitation model learns the policy of a human supervisor as a function of laser light detection and ranging (LIDAR) data, which is then used in real time to drive a robot in an autonomous fashion in a laboratory setting. The performance of the proposed model for imitation learning is compared with that of several other state-of-the-art methods, reported in the machine learning literature, for spatial and temporal modeling. The learned policy is implemented on a robot using a Nvidia Jetson TX2 board which, in turn, is validated on test tracks. The proposed spatio-temporal model outperforms several other off-the-shelf machine learning techniques to learn the policy. View Full-Text
Keywords: autonomous robots; neural networks; imitation learning autonomous robots; neural networks; imitation learning
Show Figures

Figure 1

MDPI and ACS Style

Fu, Y.; Jha, D.K.; Zhang, Z.; Yuan, Z.; Ray, A. Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot. Machines 2019, 7, 24.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop