Perceiving Systems.js

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/*
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	Copyright © 2021 Abe Jellinek
	
	This file is part of Zotero.

	Zotero is free software: you can redistribute it and/or modify
	it under the terms of the GNU Affero General Public License as published by
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	(at your option) any later version.

	Zotero is distributed in the hope that it will be useful,
	but WITHOUT ANY WARRANTY; without even the implied warranty of
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	GNU Affero General Public License for more details.

	You should have received a copy of the GNU Affero General Public License
	along with Zotero. If not, see <http://www.gnu.org/licenses/>.

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						"lastName": "Saini",
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						"lastName": "Bonetto",
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					{
						"firstName": "Michael",
						"lastName": "Pabst",
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					{
						"firstName": "Yu Tang",
						"lastName": "Liu",
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						"firstName": "Michael",
						"lastName": "Black",
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						"firstName": "Aamir",
						"lastName": "Ahmad",
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				"date": "2020-10",
				"DOI": "https://doi.org/10.1109/LRA.2020.3013906",
				"abstractNote": "In this letter, we introduce a deep reinforcement learning (DRL) based multi-robot formation controller for the task of autonomous aerial human motion capture (MoCap). We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose, and shape of a single moving person using multiple micro aerial vehicles. State-of-the-art solutions to this problem are based on classical control methods, which depend on hand-crafted system, and observation models. Such models are difficult to derive, and generalize across different systems. Moreover, the non-linearities, and non-convexities of these models lead to sub-optimal controls. In our work, we formulate this problem as a sequential decision making task to achieve the vision-based motion capture objectives, and solve it using a deep neural network-based RL method. We leverage proximal policy optimization (PPO) to train a stochastic decentralized control policy for formation control. The neural network is trained in a parallelized setup in synthetic environments. We performed extensive simulation experiments to validate our approach. Finally, real-robot experiments demonstrate that our policies generalize to real world conditions.",
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				"publicationTitle": "IEEE Robotics and Automation Letters",
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				"url": "https://ieeexplore.ieee.org/document/9158379",
				"volume": "5",
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				"notes": [
					{
						"note": "<p>Also accepted and presented in the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).</p>"
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