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  <title>meow6</title>

  <meta name="author" content="Liu Cao">
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                  <p class="name" style="text-align: center;">
                    Liu Cao
                  </p>
                  <p style="text-align:center">
                    <a href="l-cao20@mails.tsinghua.edu.cn">Email</a> &nbsp;/&nbsp;
                    <a href="https://scholar.google.com/citations?user=LmGYApoAAAAJ&hl=en">Scholar</a> &nbsp;/&nbsp;
                    <a href="https://github.com/xiaohu-art">Github</a>
                  </p>
                  <p style="font-size: 18px;">
                    Hello there! I'm Liu Cao, currently a first-year PhD student in <a
                      style="font-weight: bold; font-size: 20px;" href="https://iiis.tsinghua.edu.cn/">IIIS</a>,
                    Tsinghua University, advised by Prof. <a style="font-weight: bold; font-size: 20px;"
                      href="https://mxu34.github.io/">Mengdi Xu</a> on <strong
                      style="font-weight: bold; font-size: 20px;">Humanoid Robots</strong> and <strong
                      style="font-weight: bold; font-size: 20px;">Whole-body Control</strong>. Before that, I received
                    my bachelor's degree from <a style="font-weight: bold; font-size: 20px;"
                      href="https://www.ee.tsinghua.edu.cn/">EE</a>, Tsinghua University.
                    <br>
                    <br>
                    My current interests lie in developing autonomous robots, particularly humanoid and mobile
                    manipulators, capable of active perception and interacting in the real-world scenarios.
                  </p>
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                  <a href="images/avator.jpg"><img
                      style="width:100%;max-width:100%;object-fit: cover; border-radius: 50%;" alt="profile photo"
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                  <h2><strong style="font-weight: bold; font-size: 20px;">Research</strong></h2>
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                  <video width="105%" height="105%" muted="" autoplay="" loop="">
                    <source src="images/powersaving.mp4" type="video/mp4">
                    Your browser does not support the video tag.
                  </video>
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                <td class="paper-detail" style="padding:8px;width:80%;vertical-align:middle">
                  <a href="https://hard-to-sim.github.io/">
                    <span class="papertitle">Fine-Tuning Hard-to-Simulate Objectives for Quadruped Locomotion:
                      A Case Study on Total Power Saving
                    </span>
                  </a>
                  <br>
                  <a href="https://richard-coder-nai.github.io/">Ruiqian Nai</a>,
                  Jiacheng You,
                  <strong>Liu Cao</strong>,
                  <a href="https://morning-star-7.github.io/">Hanchen Cui</a>,
                  Shiyuan Zhang,
                  <a href="http://hxu.rocks/">Huazhe Xu</a>,
                  <a href="https://yang-gao.weebly.com/">Yang Gao</a>,
                  <br>
                  <em>IEEE International Conference on Robotics and Automation (ICRA)</em>, 2025
                  <br>
                  <a href="https://junfeng-long.github.io/HIMLoco/">project page</a>
                  /
                  <a href="https://arxiv.org/pdf/2312.11460">arXiv</a>
                  <p></p>
                  <p>
                    Better locomotion using real-world data. Our approach achieves a 24-28% net reduction in power
                    consumption
                  </p>
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                  </video>
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                <td class="paper-detail" style="padding:8px;width:80%;vertical-align:middle">
                  <a href="https://junfeng-long.github.io/HIMLoco/">
                    <span class="papertitle">Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated
                      Robot Response
                    </span>
                  </a>
                  <br>
                  <a href="https://junfeng-long.github.io/">Junfeng Long</a><sup>*</sup>,
                  Zirui Wang<sup>*</sup>,
                  <a href="https://quanyili.github.io/">Quanyi Li</a>,
                  <a href="https://gao-jiawei.com/">Jiawei Gao</a>,
                  <strong>Liu Cao</strong>,
                  <a href="https://oceanpang.github.io/">Jiangmiao Pang</a>
                  <br>
                  <em><sup>*</sup>Equal contribution</em>
                  <br>
                  <em>International Conference on Learning Representations (ICLR)</em>, 2024
                  <br>
                  <a href="https://junfeng-long.github.io/HIMLoco/">project page</a>
                  /
                  <a href="https://arxiv.org/pdf/2312.11460">arXiv</a>
                  <p></p>
                  <p>
                    We present the Hybrid Internal Model, a method enabling the control policy to estimate environmental
                    disturbances by only explicitly estimating velocity and implicitly simulating the system's response.
                  </p>
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                  <span class="papertitle">Detecting Vulnerable Nodes in Urban Infrastructure
                    Interdependent Network</span>
                  <br>
                  Jinzhu Mao<sup>*</sup>,
                  <strong>Liu Cao</strong><sup>*</sup>,
                  <a href="https://fi.ee.tsinghua.edu.cn/~gaochen/">Chen Gao</a>,
                  <a href="https://fi.ee.tsinghua.edu.cn/~wanghuandong/">Huandong Wang</a>,
                  Hangyu Fan,
                  <a href="https://web.ee.tsinghua.edu.cn/jindepeng/zh_CN/index.htm">Depeng Jin</a>,
                  <a href="https://fi.ee.tsinghua.edu.cn/~liyong/">Yong Li</a>
                  <br>
                  <em><sup>*</sup>Equal contribution</em>
                  <br>
                  <em>ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD)</em>, 2023
                  <br>
                  <a href="https://github.com/tsinghua-fib-lab/KDD2023-ID546-UrbanInfra">code</a>
                  /
                  <a href="https://arxiv.org/pdf/2307.09866">arXiv</a>
                  <p>
                    We model the interdependent network as a heterogeneous graph and propose a system based on graph
                    neural network with reinforcement learning, which can be trained on real-world data, to characterize
                    the vulnerability of the city system accurately.
                  </p>
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