<story><title>RoboAgent: A universal agent with 12 Skills</title><url>https://robopen.github.io/</url></story><parent_chain></parent_chain><comment><author>Animats</author><text>The 12 skills are&lt;p&gt;- Flap open&amp;#x2F;close (oven door)&lt;p&gt;- Slide open&amp;#x2F;close (drawer)&lt;p&gt;- Slide in&amp;#x2F;out (slideable shelf)&lt;p&gt;- Cap open&amp;#x2F;close (screw caps)&lt;p&gt;- Pick&amp;#x2F;place (for relatively easy situations on flat surfaces, not bin picking)&lt;p&gt;- Wipe&lt;p&gt;&amp;quot;Wipe&amp;quot; doesn&amp;#x27;t have a reverse operation, and #12 is not illustrated.&lt;p&gt;The main thing here is that the robot has much force sensing, and interpreting that data is hard. So they had people do this with a teleoperator with force feedback, and fed that into a machine learning system. What this is really about is how to  use force feedback data effectively. People have struggled with this for key-in-lock and part assembly for decades.&lt;p&gt;Seems like good work on a longstanding hard problem, but not a breakthrough.</text></comment>
<story><title>RoboAgent: A universal agent with 12 Skills</title><url>https://robopen.github.io/</url></story><parent_chain></parent_chain><comment><author>krasin</author><text>Related: RT-2 ([1]). The RoboAgent compares itself with RT-1, but not with RT-2, which is a big improvement upon RT-1.&lt;p&gt;Disclaimer: I work in the same group that made RT-1 and RT-2, but not a direct contributor to that work.&lt;p&gt;1. &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;www.deepmind.com&amp;#x2F;blog&amp;#x2F;rt-2-new-model-translates-vision-and-language-into-action&quot; rel=&quot;nofollow noreferrer&quot;&gt;https:&amp;#x2F;&amp;#x2F;www.deepmind.com&amp;#x2F;blog&amp;#x2F;rt-2-new-model-translates-visi...&lt;/a&gt;</text></comment>