Merge pull request #98 from ucsdwcsng/satyam-dev

update for videos on page

_layouts/publication.html

@@ -523,6 +523,23 @@ <h2>Press Coverage</h2>
               </center>
               {% endfor %}
 
+              <div style="display: flex; flex-wrap: wrap; gap: 20px; margin-top: 20px;">
+                {% for video in page.driving_video %}
+                  <div style="flex: 1 1 calc(50% - 20px); background-color: #f9f9f9; padding: 10px; border: 1px solid #ddd; border-radius: 8px;">
+                    <div style="position: relative; padding-bottom: 56.25%; height: 0; overflow: hidden; border-radius: 8px;">
+                      <iframe 
+                        src="{{ video.video_link }}" 
+                        style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border: none; border-radius: 8px;" 
+                        allowfullscreen>
+                      </iframe>
+                    </div>
+                    <p style="font-size: 14px; color: #555; text-align: center; margin-top: 10px;">
+                      {{ video.desc }}
+                    </p>
+                  </div>
+                {% endfor %}
+              </div>              
+
               <!-- <center><h1>Planning to Explore</h1></center>
       <div style="width:800px; margin:0 auto; text-align=right">
         The agent first leverages planning to explore in self-supervised manner, without task-specific rewards, to learn a global world model. After the exploration phase, it receives reward functions to adapt to multiple tasks, such as standing, walking, running, and using either zero or few tasks-specific interactions.

_posts/2024-12-10-c-shenron.md

@@ -38,7 +38,10 @@ description: # all combinations are possible: (title+text+image, title+image, te
       text: "The advancement of self-driving technology has become a focal point in outdoor robotics, driven by the need for robust and efficient perception systems. This paper addresses the critical role of sensor integration in autonomous vehicles, particularly emphasizing the underutilization of radar compared to cameras and LiDARs. While extensive research has been conducted on the latter two due to the availability of large-scale datasets, radar technology offers unique advantages such as all-weather sensing and occlusion penetration, which are essential for safe autonomous driving. This study presents a novel integration of a realistic radar sensor model within the CARLA simulator, enabling researchers to develop and test navigation algorithms using radar data. Utilizing this radar sensor and showcasing its capabilities in simulation, we demonstrate improved performance in end-to-end driving scenarios. Our findings aim to rekindle interest in radar-based self-driving research and promote the development of algorithms that leverage radar's strengths."
 
 driving_video: # to add the driving video 
-    - route_des: # add a description for the route here
-    - video_link: # provide the video link
+    - desc: placeholder
+      video_link: https://www.youtube.com/embed/ZC8FzF_mzpY?si=dfd4X2T-N4IoerUP
+    - desc: placeholder
+      video_link: https://www.youtube.com/embed/ZC8FzF_mzpY?si=dfd4X2T-N4IoerUP
+
 
 ---