<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training · HackerPapers discussions</title><link>https://hackerpaper.kaipfister.de/p/2209.11133/</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/2209.11133/feed.xml" rel="self" type="application/rss+xml" /><item><title>PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training — mentioned by madratman</title><link>https://news.ycombinator.com/item?id=33367698</link><guid isPermaLink="false">urn:hackerpapers:mention:2209.11133:33367698</guid><pubDate>Fri, 28 Oct 2022 04:31:26 GMT</pubDate><description>&lt;p&gt;Generative self-supervised model for robotics data. Highlights: - Transformer model for autoregressive prediction of states and actions over time to implicitly encode dynamics and behaviors for a particular robot - The representation learned can be fine-tuned to distinct tasks (navigation, mapping, localization) with minimal data - The base transformer model serves as a generative robotics model, similarly to GPT-3, and can be prompted to result in different robot behaviors Paper: https://arxiv.org/abs/2209.11133; Video: https://youtu.be/mNQvQu_atuw; Code and data…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2209.11133/"&gt;Paper and discussion history&lt;/a&gt;. A mention is not an endorsement.&lt;/p&gt;</description><category>cs.RO</category></item><item><title>PACT: Perception-Action Causal Transformer for Autoregressive Robotics Pre-Training — mentioned by CheapBreakfast9</title><link>https://news.ycombinator.com/item?id=33365009</link><guid isPermaLink="false">urn:hackerpapers:mention:2209.11133:33365009</guid><pubDate>Thu, 27 Oct 2022 22:44:41 GMT</pubDate><description>&lt;p&gt;Generative self-supervised model for robotics data. Highlights: - Transformer model for autoregressive prediction of states and actions over time to implicitly encode dynamics and behaviors for a particular robot - The representation learned can be fine-tuned to distinct tasks (navigation, mapping, localization) with minimal data - The base transformer model serves as a generative robotics model, similarly to GPT-3, and can be prompted to result in different robot behaviors Paper: https://arxiv.org/abs/2209.11133 Video: https://youtu.be/mNQvQu_atuw Code and data…&lt;/p&gt;&lt;p&gt;&lt;a href="https://hackerpaper.kaipfister.de/p/2209.11133/"&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>
