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<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/1907.04799/</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/1907.04799/feed.xml" rel="self" type="application/rss+xml" /><item><title>RL-RRT: Kinodynamic Motion Planning via Learning Reachability Estimators from RL Policies — mentioned by andreyk</title><link>https://news.ycombinator.com/item?id=28264202</link><guid isPermaLink="false">urn:hackerpapers:mention:1907.04799:28264202</guid><pubDate>Sun, 22 Aug 2021 10:02:56 GMT</pubDate><description>&lt;p&gt;I think this is a bit too cynical/negative. It&amp;#x27;s not true that neural net approaches don&amp;#x27;t work, they just have different strengths and weaknesses (strength being generalization/flexibility/ease of use, weakness being long term coherence, interpretability, reliability). There are plenty of examples of traditional methods being combined with neural net methods (eg https://ai.stanford.edu/blog/qagnn/ , https://arxiv.org/abs/1907.04799 , NeRF), and I think that is likely to happen in this context as well to some extent.&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/1907.04799/"&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>
