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<!DOCTYPE html>
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GCBF+: A Neural Graph Control Barrier Function Framework for Distributed Safe Multi-Agent Control
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<h1 class="title is-1 publication-title">Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://syzhang092218-source.github.io/">Songyuan Zhang</a>,</span>
<span class="author-block">
<a href="https://kunalgarg.mit.edu/">Kunal Garg</a>,</span>
<span class="author-block">
<a href="http://chuchu.mit.edu/">Chuchu Fan</a></span>
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<span class="author-block">Massachusetts Institute of Technology</span>
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<h2 class="title is-4">For an improved version of GCBF, check our <a href="https://mit-realm.github.io/gcbfplus-website/"> GCBF+</a>!</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals. This paper addresses the problem of collision avoidance, scalability, and generalizability by introducing graph control barrier functions (GCBFs) for distributed control. The newly introduced GCBF is based on the well-established CBF theory for safety guarantees but utilizes a graph structure for scalable and generalizable decentralized control. We use graph neural networks to learn both neural a GCBF certificate and distributed control. We also extend the framework from handling state-based models to directly taking point clouds from LiDAR for more practical robotics settings. We demonstrated the efficacy of GCBF in a variety of numerical experiments, where the number, density, and traveling distance of agents, as well as the number of unseen and uncontrolled obstacles increase. Empirical results show that GCBF outperforms leading methods such as MAPPO and multi-agent distributed CBF (MDCBF). Trained with only 16 agents, GCBF can achieve up to 3 times improvement of success rate (agents reach goals and never encountered in any collisions) on < 500 agents, and still maintain more than 50% success rates for > 1000 agents when other methods completely fail.
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<h2 class="title is-3">Experiments</h2>
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<b>
GCBF controller in the Dubin's Car environment trained with 16 agents and tested with 32/128 agents
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GCBF controller with obstacles
</b>
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<h2 class="title is-3">Algorithm Structure</h2>
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<p>
We design the <b>node features</b> to be the indicators of the types of the nodes (agent/LiDAR hitting point/goal), and <b>edge features</b> to be the relative positions, velocities, etc. The information is processed by the graph neural network with attention, which outputs the CBF value \(h_i\) and the collision-avoidance control \(u_i^\mathrm{NN}\). The learned CBF <b>determines</b> whether to use the nominal controller \(u_i^\mathrm{nom}\) or <b>switch</b> to the collision-avoidance controller \(u_i^\mathrm{NN}\).
</p>
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The learned CBF contour with the attention value.
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<h2 class="title is-3">Numerical Results</h2>
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alt="algorithm structure"/>
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GCBF outperforms the baselines across the three environments and the three sets of experiments, namely, increasing density of the agents in a fixed workspace, increasing the size of the workspace to keep the density same, and increasing the size of the workspace but limiting the average distance traveled by agents. GCBF outperforms them because of a <b>better structure</b> than MDCBF, and RL <b>sacrifies</b> safety.
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<h2 class="title is-3">Related Work</h2>
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This work is the fundation of our work <a href="https://mit-realm.github.io/gcbfplus-website/">GCBF+</a>.
For a survey of the field of learning safe control for multi-robot systems, see <a rel="survey" href="https://arxiv.org/pdf/2311.13714.pdf">this paper</a>.
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{zhang2023gcbf,
title={Neural graph control barrier functions guided distributed collision-avoidance multi-agent control},
author={Zhang, Songyuan and Garg, Kunal and Fan, Chuchu},
booktitle={Conference on Robot Learning},
pages={2373--2392},
year={2023},
organization={PMLR}
}</code></pre>
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