A bit more meat on the page.

docs/index.html

@@ -100,7 +100,7 @@ <h1>SOUS VIDE</h1>
         </div> -->
         <div class="section">
             <h2>Demonstration</h2>
-            <p>Watch the videos below for a demonstration of our project in action:</p>
+            <p>Main Videos:</p>
             <div class="video-container">
                 <iframe src="https://youtu.be/IhZeXXJ47Js" frameborder="0" allowfullscreen></iframe>
             </div>
@@ -166,8 +166,32 @@ <h2>Getting Started</h2>
 
         <div class="section">
             <h2>Results</h2>
-            <p>Our findings demonstrate [key results], achieving [specific performance metrics or outcomes]. Below is a visualization of one of our key results:</p>
-            <img src="results/figure1.png" alt="Key Results" style="max-width: 100%; border: 1px solid #ddd;">
+            <p>This work introduces SOUS VIDE, a novel training
+                paradigm leveraging Gaussian Splatting and lightweight vi-
+                suomotor policy architectures for end-to-end drone navigation.
+                By coupling high-fidelity visual data synthesis with online
+                adaptation mechanisms, SOUS VIDE achieves zero-shot sim-
+                to-real transfer, demonstrating remarkable robustness to varia-
+                tions in mass, thrust, lighting, and dynamic scene changes. Our
+                experiments underscore the policy’s ability to generalize across
+                diverse scenarios, including complex and extended trajectories,
+                with graceful degradation under extreme conditions. Notably,
+                the integration of a streamlined adaptation module enabled the
+                policy to overcome limitations of prior visuomotor approaches,
+                offering a computationally efficient yet effective solution for
+                addressing model inaccuracies.
+                These findings highlight the potential of SOUS VIDE as
+                a foundation for future advancements in autonomous drone
+                navigation. While its robustness and versatility are evident,
+                challenges such as inconsistent performance in multi-objective
+                tasks suggest opportunities for improvement through more
+                sophisticated objective encodings. Further exploration into
+                scaling the approach to more complex environments and in-
+                corporating additional sensory modalities could enhance both
+                adaptability and reliability. Ultimately, this work paves the
+                way for deploying learned visuomotor policies in real-world
+                applications, bridging the gap between simulation and practical
+                autonomy in drone operations.</p>
         </div>
 
         <div class="section">