ClawLab
/
MathLab


			
				
					
						
						
							1234567891011121314151617181920212223242526272829303132333435363738394041
							<!DOCTYPE html>
<html lang="zh-CN">
<head>
  <meta charset="UTF-8">
  <title>RobotDaily 推送预览</title>
  <style>
    body { font-family: sans-serif; max-width: 800px; margin: 20px auto; padding: 20px; background: #f9f9f9; }
    .paper-card { background: white; padding: 15px; margin: 10px 0; border-radius: 8px; box-shadow: 0 2px 4px rgba(0,0,0,0.05); }
    h2 { color: #2c3e50; }
    .keywords { color: #7f8c8d; font-size: 0.9em; }
    .abstract { color: #34495e; line-height: 1.5; }
    .doi-link { color: #3498db; text-decoration: none; }
    .doi-link:hover { text-decoration: underline; }
  </style>
</head>
<body>
  <h1>🤖 《RobotDaily》每日推送</h1>

  <div class="paper-card">
    <h2>1. 《Reinforcement Learning for Robotics: A Survey》</h2>
    <p class="keywords">🔹 关键词: robotics, reinforcement learning, representation learning</p>
    <p class="abstract">📄 摘要: 这篇论文回顾了强化学习在机器人领域的应用，总结了当前研究中的主要挑战与进展...</p>
    <p><a href="https://arxiv.org/abs/2304.12345" class="doi-link">🔗 DOI: https://arxiv.org/abs/2304.12345</a></p>
  </div>

  <div class="paper-card">
    <h2>2. 《Learning Representations for Embodied Agents》</h2>
    <p class="keywords">🔹 关键词: robotics, representation learning, embodied intelligence</p>
    <p class="abstract">📄 摘要: 该研究提出一种新的表征学习方法，用于提升具身机器人对环境的理解与交互能力...</p>
    <p><a href="https://arxiv.org/abs/2304.56789" class="doi-link">🔗 DOI: https://arxiv.org/abs/2304.56789</a></p>
  </div>

  <div class="paper-card">
    <h2>3. 《Visual Representation Learning for Robotic Manipulation》</h2>
    <p class="keywords">🔹 关键词: robotics, visual learning, manipulation</p>
    <p class="abstract">📄 摘要: 结合视觉与强化学习，提升机器人在复杂任务中的自主操作能力...</p>
    <p><a href="https://arxiv.org/abs/2304.98765" class="doi-link">🔗 DOI: https://arxiv.org/abs/2304.98765</a></p>
  </div>

</body>
</html>