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Author: faikit

app/(default)/(event)/challenge2026temp/page.tsx

@@ -175,7 +175,7 @@ export default function Home() {
             <div className="w-full px-6 flex justify-center mt-6">
                 <div className="w-full max-w-7xl flex flex-col gap-3">
                     <p className="leading-relaxed">
-                        Deformable Object Manipulation, particularly the <span className="underline">handling of textiles</span> such as clothing, remains one of the most fundamental and unsolved challenges in robotics. Unlike rigid objects, deformable materials exhibit effectively infinite degrees of freedom and highly non-linear dynamics, making perception, modeling, and control intrinsically difficult. The challenge is designed as a scientific instrument to assess a system's full-stack capability. Participants are tasked with developing end-to-end solutions for flat folding garments. This task requires the coordinated integration of computer vision, control theory, and learning-based methods such as deep reinforcement learning, under realistic physical constraints.
+                        Deformable Object Manipulation, particularly the <span className="underline">handling of textiles</span> such as clothing, remains one of the most fundamental and unsolved challenges in robotics. Unlike rigid objects, deformable materials exhibit effectively infinite degrees of freedom and highly non-linear dynamics, making perception, modeling, and control intrinsically difficult. Despite recent progress, many existing approaches rely heavily on simplified simulation environments or reduce manipulation to isolated pick-and-place primitives, which fall far short of capturing the complex, continuous, and history-dependent dynamics of real-world. The challenge is designed as a scientific instrument to assess a system's full-stack capability. Participants are tasked with developing end-to-end solutions for flat folding garments. This task requires the coordinated integration of computer vision, control theory, and learning-based methods such as deep reinforcement learning, under realistic physical constraints.
                     </p>
                 </div>
             </div>