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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>CAD2RL: Real Single-Image Flight without a Single Real Image · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1611.04201/</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/1611.04201/feed.xml" rel="self" type="application/rss+xml" /><item><title>CAD2RL: Real Single-Image Flight without a Single Real Image — mentioned by alexleegk</title><link>https://news.ycombinator.com/item?id=14319072</link><guid isPermaLink="false">urn:hackerpapers:mention:1611.04201:14319072</guid><pubDate>Thu, 11 May 2017 20:10:03 GMT</pubDate><description>&lt;p&gt;That&amp;#x27;s what Sadeghi and Levine [1] do in their work. They train in simulation in a lot of randomly generated scenes. Since the drone is trained with such a diverse set of scenes, the learned policy generalizes to the real world. Also, note that the physics of the simulation doesn&amp;#x27;t even need to be realistic. Unless you are doing high-speed control or aggressive maneuvers, the challenging part is the perception and not the control. In the paper from OP, the controls are even high-level discrete actions: left, forward, right. [1] https://arxiv.org/abs/1611.04201&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1611.04201/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.LG</category></item></channel></rss>
