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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Domain Randomization and Generative Models for Robotic Grasping · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1710.06425/</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/1710.06425/feed.xml" rel="self" type="application/rss+xml" /><item><title>Domain Randomization and Generative Models for Robotic Grasping — mentioned by DavidWilkinson</title><link>https://news.ycombinator.com/item?id=21738495</link><guid isPermaLink="false">urn:hackerpapers:mention:1710.06425:21738495</guid><pubDate>Sun, 08 Dec 2019 21:34:18 GMT</pubDate><description>&lt;p&gt;This is a great example. Relatedly Josh Tobin and team (OpenAI) have done some awesome recent work on how domain randomization can be applied to simulated worlds to improve the generalizability of algorithms as well: https://arxiv.org/abs/1710.06425&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1710.06425/"&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>
