Updated text content inline with final version of ICRA paper

- updated abstract and comparison results

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@@ -92,7 +92,7 @@ <h1 class="title is-1 publication-title">DiPPeR: Diffusion-based 2D Path Planner
         <h3 class="title is-2">Abstract</h3>
         <div class="content has-text-justified">
           <p>
-           In this work, we present DiPPeR, a novel and fast 2D path planning framework for quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a scalable dataset of map images  and corresponding end-to-end trajectories, an image-conditioned diffusion planner for mobile robots, and a training/inference pipeline employing CNNs. We validate our approach in several mazes, as well as in real-world deployment scenarios on Boston Dynamic's Spot and Unitree's Go1 robots. DiPPeR performs on average 70 times faster for trajectory generation against both search based and data driven path planning algorithms with an average of 80% consistency in producing feasible paths of various lengths in maps of variable size, and obstacle structure.
+            In this work, we present DiPPeR, a novel and fast 2D path planning framework for quadrupedal locomotion, leveraging diffusion-driven techniques. Our contributions include a scalable dataset generator for map images and corresponding trajectories, an image-conditioned diffusion planner for mobile robots, and a training/inference pipeline employing CNNs. We validate our approach in several mazes, as well as in real-world deployment scenarios on Boston Dynamic's Spot and Unitree's Go1 robots. DiPPeR performs on average 23 times faster for trajectory generation against both search based and data driven path planning algorithms with an average of 87% consistency in producing feasible paths of various length in maps of variable size, and obstacle structure.
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@@ -268,9 +268,9 @@ <h5>
           <h3 class="title" style="text-align: center; padding-bottom: 7px;">MRPB Dataset</h3>
           <h5>
             <center>
-             Dataset used to compare DiPPeR's inference speed with that of A* and N-A*.  
+             Dataset used to compare DiPPeR's inference speed with that of A*, N-A* and ViT-A*.  
              DiPPeR's inference time is 0.4s for all maps, regardless of their size,  
-             which is on average 70 times faster than the SOTA planners. 
+             which is on average 23 times faster against the next best performing SOTA planners. 
             </center>
           </h5>
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