Perceptual Attention-based Predictive Control

Keuntaek Lee, Gabriel Nakajima An, Viacheslav Zakharov, Evangelos A. Theodorou
Proceedings of the Conference on Robot Learning, PMLR 100:220-232, 2020.

Abstract

In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing architecture is used to support a perceptual attention-based predictive control algorithm that leverages model predictive control (MPC), convolutional neural networks (CNNs), and uncertainty quantification methods. The novelty of our approach lies in using MPC to learn how to place attention on relevant areas of the visual input, which ultimately allows the system to more rapidly detect unsafe conditions. We accomplish this by using MPC to learn to select regions of interest in the input image, which are used to output control actions as well as estimates of epistemic and aleatoric uncertainty in the attention-aware visual input. We use these uncertainty estimates to quantify the safety of our network controller under the current navigation condition. The proposed architecture and algorithm is tested on a 1:5 scale terrestrial vehicle. Experimental results show that the proposed algorithm outperforms previous approaches on early detection of unsafe conditions, such as when novel obstacles are present in the navigation environment. The proposed architecture is the first step towards using deep learning-based perceptual control policies in safety-critical domains.

Cite this Paper

BibTeX
@InProceedings{pmlr-v100-lee20b, title = {Perceptual Attention-based Predictive Control}, author = {Lee, Keuntaek and An, Gabriel Nakajima and Zakharov, Viacheslav and Theodorou, Evangelos A.}, booktitle = {Proceedings of the Conference on Robot Learning}, pages = {220--232}, year = {2020}, editor = {Kaelbling, Leslie Pack and Kragic, Danica and Sugiura, Komei}, volume = {100}, series = {Proceedings of Machine Learning Research}, month = {30 Oct--01 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v100/lee20b/lee20b.pdf}, url = {https://proceedings.mlr.press/v100/lee20b.html}, abstract = {In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing architecture is used to support a perceptual attention-based predictive control algorithm that leverages model predictive control (MPC), convolutional neural networks (CNNs), and uncertainty quantification methods. The novelty of our approach lies in using MPC to learn how to place attention on relevant areas of the visual input, which ultimately allows the system to more rapidly detect unsafe conditions. We accomplish this by using MPC to learn to select regions of interest in the input image, which are used to output control actions as well as estimates of epistemic and aleatoric uncertainty in the attention-aware visual input. We use these uncertainty estimates to quantify the safety of our network controller under the current navigation condition. The proposed architecture and algorithm is tested on a 1:5 scale terrestrial vehicle. Experimental results show that the proposed algorithm outperforms previous approaches on early detection of unsafe conditions, such as when novel obstacles are present in the navigation environment. The proposed architecture is the first step towards using deep learning-based perceptual control policies in safety-critical domains.} }
Endnote
%0 Conference Paper %T Perceptual Attention-based Predictive Control %A Keuntaek Lee %A Gabriel Nakajima An %A Viacheslav Zakharov %A Evangelos A. Theodorou %B Proceedings of the Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2020 %E Leslie Pack Kaelbling %E Danica Kragic %E Komei Sugiura %F pmlr-v100-lee20b %I PMLR %P 220--232 %U https://proceedings.mlr.press/v100/lee20b.html %V 100 %X In this paper, we present a novel information processing architecture for safe deep learning-based visual navigation of autonomous systems. The proposed information processing architecture is used to support a perceptual attention-based predictive control algorithm that leverages model predictive control (MPC), convolutional neural networks (CNNs), and uncertainty quantification methods. The novelty of our approach lies in using MPC to learn how to place attention on relevant areas of the visual input, which ultimately allows the system to more rapidly detect unsafe conditions. We accomplish this by using MPC to learn to select regions of interest in the input image, which are used to output control actions as well as estimates of epistemic and aleatoric uncertainty in the attention-aware visual input. We use these uncertainty estimates to quantify the safety of our network controller under the current navigation condition. The proposed architecture and algorithm is tested on a 1:5 scale terrestrial vehicle. Experimental results show that the proposed algorithm outperforms previous approaches on early detection of unsafe conditions, such as when novel obstacles are present in the navigation environment. The proposed architecture is the first step towards using deep learning-based perceptual control policies in safety-critical domains.
APA
Lee, K., An, G.N., Zakharov, V. & Theodorou, E.A.. (2020). Perceptual Attention-based Predictive Control. Proceedings of the Conference on Robot Learning, in Proceedings of Machine Learning Research 100:220-232 Available from https://proceedings.mlr.press/v100/lee20b.html.

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