<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning |</title><link>https://relo02.github.io/tags/machine-learning/</link><atom:link href="https://relo02.github.io/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><description>Machine Learning</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://relo02.github.io/media/icon_hu_982c5d63a71b2961.png</url><title>Machine Learning</title><link>https://relo02.github.io/tags/machine-learning/</link></image><item><title>Bayesian Optimization for Learning Nonlinear MPC in Autonomous Agent Navigation</title><link>https://relo02.github.io/publications/bayesian-mpc/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://relo02.github.io/publications/bayesian-mpc/</guid><description>&lt;p&gt;First-author work (with G. Voss, G. Beltrami, F. Dorati, and T. F. Banfi) presented at the
&lt;strong&gt;IEEE ICRA 2026 Workshop&lt;/strong&gt;. We learn the weights of a nonlinear MPC navigation controller via
&lt;strong&gt;Bayesian Optimization&lt;/strong&gt;, treating closed-loop performance as a black-box objective and deploying
the result on a &lt;strong&gt;Unitree Go2&lt;/strong&gt; quadruped.&lt;/p&gt;
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&lt;li&gt;💻
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