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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1610.03518/</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/1610.03518/feed.xml" rel="self" type="application/rss+xml" /><item><title>Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model — mentioned by deepnet</title><link>https://news.ycombinator.com/item?id=14348895</link><guid isPermaLink="false">urn:hackerpapers:mention:1610.03518:14348895</guid><pubDate>Tue, 16 May 2017 12:03:58 GMT</pubDate><description>&lt;p&gt;&amp;quot;However, a policy that succeeds in simulation often doesn&amp;#x27;t work when deployed on a real robot. Nevertheless, often the overall gist of what the policy does in simulation remains valid in the real world. In this paper we investigate such settings, where the sequence of states traversed in simulation remains reasonable for the real world, even if the details of the controls are not, as could be the case when the key differences lie in detailed friction, contact, mass and geometry properties&amp;quot; from Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1610.03518/"&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>
