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kunalgarg42 committed Mar 27, 2024
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<head>
<meta charset="utf-8">
<meta name="description"
content="GCBF+">
content="LLM-based deadlock resolution">
<meta name="keywords" content="Multi-agent systems; Distributed control; Safe control; Control barrier functions; Graph neural networks">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>GCBF+</title>
<title>LLM-based deadlock resolution</title>

<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
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<h2 class="title is-3">Evaluations</h2>
<div class="content">
<div class="content has-text-justified">
(Top plot) 20 instances of the Randomized Room environment and Randomized environment (bottom plot)
(Top plot) 20 instances of the Randomized Room environment and Randomized environment (bottom plot) <br>

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<b>
We evaluate the performance of the LLM-based high-level planner in two environments: Randomized Room and Randomized.
Frequency (left axis) of trials in which N agents reach their goal for (shown in thin colored bars), and the average number of agents reaching their goal per trial (right axis) for each tested high-level planner (shown in the thick gray bar). </b>
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LLM-based high level planner in action. The LLM suggests a leader and a direction for it to move along when the agents get stuck in a deadlock.
</b>
<center>
<video width="33%" autoplay="" muted="" loop="">
<source src="vids/LLM_zero_shot.mp4" type="video/mp4">
<video width="50%" autoplay="" muted="" loop="">
<source src="vids/LLM_in_action.mp4" type="video/mp4">
</video>
</center>

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