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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width,initial-scale=1" />
<title>NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-Based Preference Rewards</title>
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</head>
<body class="min-h-screen bg-gradient-to-b from-slate-900 via-slate-800 to-slate-900 text-white">
<header class="relative overflow-hidden">
<div class="absolute inset-0 bg-gradient-to-r from-blue-600/20 to-purple-600/20"></div>
<div class="container mx-auto px-6 py-20 relative z-10">
<div class="max-w-5xl mx-auto text-center">
<div class="mb-8 flex justify-center">
<img src="https://declare-lab.github.io/assets/images/nora1.5-new.png" alt="NORA Logo" class="w-40 h-40 object-contain" />
</div>
<h1 class="text-6xl font-bold mb-6 bg-clip-text text-transparent bg-gradient-to-r from-blue-400 to-purple-400">NORA-1.5</h1>
<p class="text-2xl text-slate-300 mb-4">A Vision-Language-Action Model Trained using <br> World Model- and Action-Based Preference Rewards</p>
<p class="text-lg text-slate-400 mb-12 max-w-3xl mx-auto">Shows strong generalizability even with limited fine-tuning data</p>
<div class="flex flex-wrap justify-center gap-4 mb-16">
<!-- GitHub -->
<a href="https://github.com/declare-lab/nora-1.5"
class="flex items-center gap-2 bg-white text-slate-900 px-8 py-4 rounded-lg font-semibold hover:bg-slate-100 transition-all shadow-lg">
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Code
</a>
<!-- Paper -->
<a href="https://arxiv.org/abs/2511.14659"
class="flex items-center gap-2 bg-gradient-to-r from-blue-600 to-purple-600 px-8 py-4 rounded-lg font-semibold hover:from-blue-700 hover:to-purple-700 transition-all shadow-lg">
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Paper
</a>
<!-- Demo -->
<a href="https://declare-lab.github.io/nora-1.5"
class="flex items-center gap-2 border-2 border-slate-400 px-8 py-4 rounded-lg font-semibold hover:bg-slate-800 transition-all">
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viewBox="0 0 24 24" class="w-5 h-5">
<path d="M8 5v14l11-7z"/>
</svg>
Demo
</a>
<!-- HuggingFace -->
<a href="https://huggingface.co/declare-lab/nora-1.5"
class="flex items-center gap-2 bg-yellow-400 text-slate-900 px-8 py-4 rounded-lg font-semibold hover:bg-yellow-300 transition-all shadow-lg">
🤗 HuggingFace
</a>
</div>
</div>
</div>
</header>
<!-- Overview -->
<section class="py-16 bg-slate-800/50">
<div class="container mx-auto px-6">
<div class="max-w-5xl mx-auto">
<div class="teaser">
<div class="teaser-inner">
<div class="image-placeholder" id="teaser-placeholder">
<img src="https://declare-lab.github.io/assets/images/nora-1.5-arxiv-teaser.png" alt="Teaser" />
</div>
<!-- <h2 class="teaser-title">Teaser Image</h2>
<div class="label">A bold visual anchor — showcasing NORA-1.5 (DPO)’s improved control.</div>
<p class="teaser-desc">Use this hero-style teaser to highlight a clean grasp frame, smoothed trajectory visualization, or any compelling preview illustrating the DPO-enhanced performance.</p>
<div class="upload-hint">Recommended: 1280×720 or larger, PNG/JPG.</div> -->
</div>
</div>
<h2 class="text-3xl font-bold text-center mb-8">Overview</h2>
<div class="bg-slate-900/50 p-8 rounded-xl border border-slate-700 mb-8">
<p class="text-lg text-slate-300 leading-relaxed mb-6">NORA-1.5 advances vision-language-action models through three key innovations: a <span class="text-blue-400 font-semibold">flow-matching action expert</span>, <span class="text-purple-400 font-semibold">action-conditioned world model rewards</span>, and <span class="text-green-400 font-semibold">DPO post-training</span>.</p>
<p class="text-lg text-slate-300 leading-relaxed">State-of-the-art results across simulation benchmarks and robust cross-embodiment transfer to Galaxea A1 robot.</p>
</div>
<h3 class="text-2xl font-bold text-center mb-6">Performance Summary</h3>
<div class="grid grid-cols-1 md:grid-cols-3 gap-6">
<div class="bg-slate-900/50 p-6 rounded-xl border border-slate-700">
<h4 class="text-lg font-bold mb-4 text-center text-blue-400">SimplerEnv (VM)</h4>
<div class="chart-wrapper">
<canvas id="overviewChart1" class="chart-canvas w-full"></canvas>
</div>
<p class="text-center mt-3 text-sm text-slate-400">+5.5% over best baseline</p>
</div>
<div class="bg-slate-900/50 p-6 rounded-xl border border-slate-700">
<h4 class="text-lg font-bold mb-4 text-center text-purple-400">LIBERO (Avg)</h4>
<div class="chart-wrapper">
<canvas id="overviewChart2" class="chart-canvas w-full"></canvas>
</div>
<p class="text-center mt-3 text-sm text-slate-400">+0.8% over best baseline</p>
</div>
<div class="bg-slate-900/50 p-6 rounded-xl border border-slate-700">
<h4 class="text-lg font-bold mb-4 text-center text-green-400">Real Robot (Success)</h4>
<div class="chart-wrapper">
<canvas id="overviewChart3" class="chart-canvas w-full"></canvas>
</div>
<p class="text-center mt-3 text-sm text-slate-400">+12.2% over NORA</p>
</div>
</div>
</div>
</div>
</section>
<!-- Performance Benchmarks -->
<section class="py-20">
<div class="container mx-auto px-6">
<div class="max-w-6xl mx-auto">
<h2 class="text-4xl font-bold text-center mb-4">Performance Benchmarks</h2>
<p class="text-center text-slate-400 mb-12">NORA-1.5 achieves state-of-the-art results across simulation and real-world benchmarks</p>
<div class="flex justify-center gap-4 mb-8 flex-wrap">
<button type="button" class="tab-btn px-6 py-3 rounded-lg font-semibold transition-all bg-blue-600 text-white" data-tab="simpler">SimplerEnv</button>
<button type="button" class="tab-btn px-6 py-3 rounded-lg font-semibold transition-all bg-slate-800 text-slate-400" data-tab="libero">LIBERO</button>
<button type="button" class="tab-btn px-6 py-3 rounded-lg font-semibold transition-all bg-slate-800 text-slate-400" data-tab="real">Real Robot</button>
<button type="button" class="tab-btn px-6 py-3 rounded-lg font-semibold transition-all bg-slate-800 text-slate-400" data-tab="realhard">Real Robot (Hard)</button>
</div>
<div class="bg-slate-900/50 p-8 rounded-xl border border-slate-700">
<div class="tab-panel" id="simpler">
<h3 class="text-2xl font-bold mb-6 text-center">SimplerEnv Visual Matching</h3>
<div class="bench-chart-box">
<!-- fixed-size canvas: 720x360 keeps "thin + taller" look consistently -->
<canvas id="simplerChart" class="bench-centered" width="720" height="360"></canvas>
</div>
</div>
<div class="tab-panel hidden" id="libero">
<h3 class="text-2xl font-bold mb-6 text-center">LIBERO Average Performance</h3>
<div class="bench-chart-box">
<canvas id="liberoChart" class="bench-centered" width="720" height="360"></canvas>
</div>
</div>
<div class="tab-panel hidden" id="real">
<h3 class="text-2xl font-bold mb-6 text-center">Galaxea A1 Real Robot Performance</h3>
<div class="bench-chart-box">
<canvas id="realChart" class="bench-centered" width="720" height="360"></canvas>
</div>
</div>
<div class="tab-panel hidden" id="realhard">
<h3 class="text-2xl font-bold mb-6 text-center">Real Robot Hard Tasks - DPO Impact</h3>
<p class="text-center text-slate-400 mb-6">Performance on challenging tasks with unseen objects and distractors</p>
<div class="bench-chart-box">
<canvas id="realHardChart" class="bench-centered" width="720" height="360"></canvas>
</div>
<div class="mt-6 grid grid-cols-2 gap-4 text-center">
<div class="bg-slate-800/50 p-4 rounded-lg">
<div class="text-2xl font-bold text-green-400">+13.08%</div>
<div class="text-sm text-slate-400">Success Rate Improvement</div>
</div>
<div class="bg-slate-800/50 p-4 rounded-lg">
<div class="text-2xl font-bold text-blue-400">+11.54%</div>
<div class="text-sm text-slate-400">Partial Success Improvement</div>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Other sections (kept concise) -->
<section class="py-20 bg-slate-800/50">
<div class="container mx-auto px-6">
<div class="max-w-6xl mx-auto">
<h2 class="text-4xl font-bold text-center mb-12">Key Contributions</h2>
<div class="grid md:grid-cols-2 gap-8">
<div class="bg-slate-900/50 p-8 rounded-xl border border-slate-700">
<h3 class="text-2xl font-bold mb-4 text-blue-400">Flow-Matching Action Expert</h3>
<p class="text-slate-300 leading-relaxed">Integrated trainable flow-matching expert with NORA backbone achieves superior performance and faster inference.</p>
</div>
<div class="bg-slate-900/50 p-8 rounded-xl border border-slate-700">
<h3 class="text-2xl font-bold mb-4 text-purple-400">World Model Rewards</h3>
<p class="text-slate-300 leading-relaxed">Action-conditioned V-JEPA2 world model provides goal-based reward signals for scalable preference optimization without simulators.</p>
</div>
<div class="bg-slate-900/50 p-8 rounded-xl border border-slate-700">
<h3 class="text-2xl font-bold mb-4 text-green-400">DPO Post-Training</h3>
<p class="text-slate-300 leading-relaxed">Direct preference optimization with hybrid rewards consistently improves performance across benchmarks.</p>
</div>
<div class="bg-slate-900/50 p-8 rounded-xl border border-slate-700">
<h3 class="text-2xl font-bold mb-4 text-orange-400">Cross-Embodiment Transfer</h3>
<p class="text-slate-300 leading-relaxed">Successfully transfers to unseen Galaxea A1 robot with strong generalization.</p>
</div>
</div>
</div>
</div>
</section>
<!-- Inserted: Teaser + Side-by-side comparison -->
<section class="container mx-auto px-6">
<div class="max-w-5xl mx-auto">
<div class="compare">
<div style="display:flex;align-items:center;justify-content:space-between;gap:12px;margin-bottom:12px">
<div style="font-weight:700">Video Demo — Baseline vs Reward-driven DPO</div>
<div style="color:var(--muted);font-size:13px">Play both videos to observe motion differences</div>
</div>
<div class="grid">
<!-- Baseline -->
<article class="card">
<div class="video-wrap">
<video controls muted playsinline preload="metadata">
<source src="https://declare-lab.github.io/assets/videos/nora1.5.mp4" type="video/mp4" />
Your browser does not support the video tag.
</video>
</div>
<div class="meta">
<div class="tag">NORA-1.5 — Baseline</div>
<div class="tag small">Erratic • Fixations • Distractor grasps</div>
</div>
<div class="tagline"><span class="highlight">Zig-zag</span> grasps — frequent corrections, lower reliability</div>
<p class="summary">The baseline model exhibits unstable behaviour: zig-zag motions, hesitation, and failed grasps due to distractor-target confusion.</p>
</article>
<!-- DPO -->
<article class="card">
<div class="video-wrap">
<video controls muted playsinline preload="metadata">
<source src="https://declare-lab.github.io/assets/videos/nora1.5-dpo.mp4" type="video/mp4" />
Your browser does not support the video tag.
</video>
</div>
<div class="meta">
<div class="tag">NORA-1.5 (DPO)</div>
<div class="tag small">Smooth • Consistent • Target-locked</div>
</div>
<div class="tagline"><span class="highlight">Smooth</span> and stable — fewer corrections, stronger success rate</div>
<p class="summary">Reward-driven DPO post-training produces smoother trajectories, consistent approach vectors, and significantly higher grasp reliability.</p>
</article>
</div>
<!-- <div style="margin-top:16px;color:var(--muted);font-size:13px">
<strong>Quick comparison:</strong>
<ul style="margin:8px 0 0 18px">
<li>Baseline: high motion variance, frequent target-loss.</li>
<li>DPO: stabilized policy with clean target acquisition.</li>
<li>Side-by-side playback highlights reduction in corrective actions.</li>
</ul>
</div> -->
</div>
</div>
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<!-- Citation & Footer -->
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<div class="max-w-4xl mx-auto bg-slate-900/50 p-8 rounded-xl border border-slate-700">
<h2 class="text-2xl font-bold mb-4">Citation</h2>
<pre class="bg-slate-950 p-6 rounded-lg overflow-x-auto text-sm text-slate-300">@article{hung2025nora15,
title={NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-Based Preference Rewards},
author={Hung, Chia-Yu and Majumder, Navonil and Deng, Haoyuan, Liu Renhang, Yankang Ang, Amir Zadeh, Chuan Li, Dorien Herremans, Ziwei Wang, and Soujanya Poria},
journal={arXiv preprint},
year={2025}
}</pre>
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<p class="text-slate-400 mb-4">Nanyang Technological University · Lambda Labs · Singapore University of Technology and Design</p>
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<a href="https://github.com/declare-lab/nora-1.5" class="text-slate-400 hover:text-white transition-colors">GitHub</a>
<a href="https://declare-lab.github.io/nora-1.5" class="text-slate-400 hover:text-white transition-colors">Project Page</a>
<a href="https://arxiv.org/abs/2511.14659" class="text-slate-400 hover:text-white transition-colors">Paper</a>
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