1.0Reinforcement Learning for Noise Steering in Diffusion-Based Driving Modelshttps://mscvprojects.ri.cmu.edu/2025fteam24rich600338<blockquote class="wp-embedded-content" data-secret="lVn5OuVLK9"><a href="https://mscvprojects.ri.cmu.edu/2025fteam24/overview/">Introduction</a></blockquote><iframe sandbox="allow-scripts" security="restricted" src="https://mscvprojects.ri.cmu.edu/2025fteam24/overview/embed/#?secret=lVn5OuVLK9" width="600" height="338" title="“Introduction” — Reinforcement Learning for Noise Steering in Diffusion-Based Driving Models" data-secret="lVn5OuVLK9" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"></iframe><script> /*! This file is auto-generated */ !function(d,l){"use strict";l.querySelector&&d.addEventListener&&"undefined"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!/[^a-zA-Z0-9]/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret="'+t.secret+'"]'),o=l.querySelectorAll('blockquote[data-secret="'+t.secret+'"]'),c=new RegExp("^https?:$","i"),i=0;i<o.length;i++)o[i].style.display="none";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute("style"),"height"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):"link"===t.message&&(r=new URL(s.getAttribute("src")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener("message",d.wp.receiveEmbedMessage,!1),l.addEventListener("DOMContentLoaded",function(){for(var e,t,s=l.querySelectorAll("iframe.wp-embedded-content"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute("data-secret"))||(t=Math.random().toString(36).substring(2,12),e.src+="#?secret="+t,e.setAttribute("data-secret",t)),e.contentWindow.postMessage({message:"ready",secret:t},"*")},!1)))}(window,document); //# sourceURL=https://mscvprojects.ri.cmu.edu/2025fteam24/wp-includes/js/wp-embed.min.js </script> Diffusion Models for Action Generation Diffusion models have recently achieved impressive results in action generation, spanning both robotic manipulation and autonomous driving. On the left, Diffusion Policy demonstrates one of the earliest successes: it reformulates control as a denoising process, generating smooth and consistent action sequences directly from image observations. By iteratively refining noisy action […]https://i.imgur.com/ZX9b7Y2.png