Deep Neural Networks for Robotics

Deep Neural Networks for Robotics

We aim to develop a platform-independent approach that utilizes deep neural networks (DNNs) to enhance classical controllers to achieve high-performance tracking. In one of our approaches, the DNNs are used as an add-on module approximating the inverse dynamics of a baseline controller to compensate for factors such as, delays or unmodeled dynamics present in the baseline system. As part of this project, based on insights obtained from control theory, we provide guidelines on the selection of DNN inputs and outputs, identify conditions when the proposed approach is effective, and derive a condition to make DNN training more efficient.

 

Related Publications

[DOI] Deep neural networks for improved, impromptu trajectory tracking of quadrotors
Q. Li, J. Qian, Z. Zhu, X. Bao, M. K. Helwa, and A. P. Schoellig
in Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 5183-5189.
[View BibTeX] [View Abstract] [Download PDF] [View Video] [More Information]
Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivative (PID) controllers, to achieve high tracking precision can be time-consuming and difficult, due to hidden dynamics and other non-idealities. The Deep Neural Network (DNN), with its superior capability of approximating abstract, nonlinear functions, proposes a novel approach for enhancing trajectory tracking control. This paper presents a DNN-based algorithm as an add-on module that improves the tracking performance of a classical feedback controller. Given a desired trajectory, the DNNs provide a tailored reference input to the controller based on their gained experience. The input aims to achieve a unity map between the desired and the output trajectory. The motivation for this work is an interactive �fly-as-you-draw� application, in which a user draws a trajectory on a mobile device, and a quadrotor instantly flies that trajectory with the DNN-enhanced control system. Experimental results demonstrate that the proposed approach improves the tracking precision for user-drawn trajectories after the DNNs are trained on selected periodic trajectories, suggesting the method�s potential in real-world applications. Tracking errors are reduced by around 40-50% for both training and testing trajectories from users, highlighting the DNNs� capability of generalizing knowledge.

@INPROCEEDINGS{li-icra17,
author = {Qiyang Li and Jingxing Qian and Zining Zhu and Xuchan Bao and Mohamed K. Helwa and Angela P. Schoellig},
title = {Deep neural networks for improved, impromptu trajectory tracking of quadrotors},
booktitle = {{Proc. of the IEEE International Conference on Robotics and Automation (ICRA)}},
year = {2017},
pages = {5183--5189},
doi = {10.1109/ICRA.2017.7989607},
urllink = {https://arxiv.org/abs/1610.06283},
urlvideo = {https://youtu.be/r1WnMUZy9-Y},
abstract = {Trajectory tracking control for quadrotors is important for applications ranging from surveying and inspection, to film making. However, designing and tuning classical controllers, such as proportional-integral-derivative (PID) controllers, to achieve high tracking precision can be time-consuming and difficult, due to hidden dynamics and other non-idealities. The Deep Neural Network (DNN), with its superior capability of approximating abstract, nonlinear functions, proposes a novel approach for enhancing trajectory tracking control. This paper presents a DNN-based algorithm as an add-on module that improves the tracking performance of a classical feedback controller. Given a desired trajectory, the DNNs provide a tailored reference input to the controller based on their gained experience. The input aims to achieve a unity map between the desired and the output trajectory. The motivation for this work is an interactive �fly-as-you-draw� application, in which a user draws a trajectory on a mobile device, and a quadrotor instantly flies that trajectory with the DNN-enhanced control system. Experimental results demonstrate that the proposed approach improves the tracking precision for user-drawn trajectories after the DNNs are trained on selected periodic trajectories, suggesting the method�s potential in real-world applications. Tracking errors are reduced by around 40-50% for both training and testing trajectories from users, highlighting the DNNs� capability of generalizing knowledge.},
}

Design of deep neural networks as add-on blocks for improving impromptu trajectory tracking
S. Zhou, M. K. Helwa, and A. P. Schoellig
in Proc. of the IEEE Conference on Decision and Control (CDC), 2017. Accepted.
[View BibTeX] [View Abstract] [Download PDF] [More Information]

This paper provides guidelines for designing deep neural networks (DNNs) as add-on blocks to baseline feedback control loops to enhance tracking performance on arbitrary, desired trajectories. The DNNs are trained to adapt the reference signals to the feedback control loop. The goal is to achieve a unity map between the desired and actual outputs. In previous work, the efficacy of this approach was demonstrated on quadrotors. On 30 unseen trajectories, the proposed DNN approach achieved an average error reduction of 43%, compared to the baseline feedback controller. Motivated by these results, this work aims to provide platform-independent design guidelines for the proposed DNN-enhanced control architecture. In particular, we provide specific guidelines for the DNN feature selection, derive conditions for when the proposed approach is effective, and show in which cases training efficiency can be further increased.

@INPROCEEDINGS{zhou-cdc17,
author={SiQi Zhou and Mohamed K. Helwa and Angela P. Schoellig},
title={Design of Deep Neural Networks as Add-on Blocks for Improving Impromptu Trajectory Tracking},
booktitle = {{Proc. of the IEEE Conference on Decision and Control (CDC)}},
year = {2017},
note={Accepted},
urllink = {https://arxiv.org/pdf/1705.10932.pdf},
abstract = {This paper provides guidelines for designing deep neural networks (DNNs) as add-on blocks to baseline feedback control loops to enhance tracking performance on arbitrary, desired trajectories. The DNNs are trained to adapt the reference signals to the feedback control loop. The goal is to achieve a unity map between the desired and actual outputs. In previous work, the efficacy of this approach was demonstrated on quadrotors. On 30 unseen trajectories, the proposed DNN approach achieved an average error reduction of 43%, compared to the baseline feedback controller. Motivated by these results, this work aims to provide platform-independent design guidelines for the proposed DNN-enhanced control architecture. In particular, we provide specific guidelines for the DNN feature selection, derive conditions for when the proposed approach is effective, and show in which cases training efficiency can be further increased.}
}

University of Toronto Institute for Aerospace Studies