<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Optimality Principles in Spacecraft Neural Guidance and Control · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2305.13078/</link><description>Collected HN comments and submissions linking this paper.</description><language>en</language><lastBuildDate>Mon, 02 Nov 2026 11:16:22 GMT</lastBuildDate><atom:link href="https://hackerpaper.kaipfister.de/p/2305.13078/feed.xml" rel="self" type="application/rss+xml" /><item><title>Optimality Principles in Spacecraft Neural Guidance and Control — mentioned by sorenjan</title><link>https://news.ycombinator.com/item?id=44763033</link><guid isPermaLink="false">urn:hackerpapers:mention:2305.13078:44763033</guid><pubDate>Fri, 01 Aug 2025 22:12:10 GMT</pubDate><description>&lt;p&gt;The last time I was reminded of the bitter lesson was when I read about Guidance &amp;amp; Control Networks, after seeing them used in an autonomous drone that beat the best human FPV pilots [0]. Basically it&amp;#x27;s using a small MLP (Multi Layer Perceptron) on the order of 200 parameters, and using the drone&amp;#x27;s state as input and controlling the motors directly with the output. We have all kinds of fancy control theory like MCP (Model Predictive Control), but it turns out that the best solution might be to train a relatively tiny NN using a mix of simulation and collected sensor data instead. It&amp;#x27;s not…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2305.13078/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.RO</category></item></channel></rss>
