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    FALCON
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        FALCON 🦅
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      <h1 class="subtitle is-1 publication-title is-size-4-mobile" style="font-size: 4rem;">
        Learning Force-Adaptive Humanoid Loco-Manipulation
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        X-Embodiment Workshop CoRL 2024
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          <h1 class="title is-1 publication-title">FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation</h1>

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              <a href="https://www.zhengyiluo.com/">Zhengyi Luo</a>*&nbsp;&nbsp;
              <a href="https://xialin-he.github.io/">Xialin He</a>*&nbsp;&nbsp;
              <a href="https://wenlixiao-cs.github.io/">Wenli Xiao</a>&nbsp;&nbsp;
              <a href="https://zita-ch.github.io/">Chong Zhang</a>&nbsp;&nbsp;
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              <a href="https://wnzhang.net/">Weinan Zhang</a>&nbsp;&nbsp;
              <a href="https://kriskitani.github.io/">Kris Kitani</a>&nbsp;&nbsp;
              <a href="https://www.cs.cmu.edu/~cliu6/">Changliu Liu</a>&nbsp;&nbsp;
              <a href="https://www.gshi.me/">Guanya Shi</a>&nbsp;&nbsp;
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              <a href="https://hang0610.github.io/">Yuanhang Zhang</a>&nbsp;&nbsp;
              <a href="https://yifuyuan.github.io/">Yifu Yuan</a>&nbsp;&nbsp;
              <a href="https://www.linkedin.com/in/prajwal-gurunath/">Prajwal Gurunath</a>&nbsp;&nbsp;
              <a href="https://tairanhe.com/">Tairan He</a>&nbsp;&nbsp;
              <br>
              <a href="https://www.linkedin.com/in/shayegan/">Shayegan Omidshafiei</a>&nbsp;&nbsp;
              <a href="https://www.linkedin.com/in/ali-agha-7aa5212a/">Ali-akbar Agha-mohammadi</a>&nbsp;&nbsp;
              <a href="https://mjvc.me/">Marcell Vazquez-Chanlatte</a>&nbsp;&nbsp;
              <a href="https://www.linkedin.com/in/liam-pedersen-0713172/">Liam Pedersen</a>&nbsp;&nbsp;
              <a href="https://www.gshi.me/">Guanya Shi</a>&nbsp;&nbsp;
            </span>
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        <h2 class="title is-2" style="text-align: center; padding-bottom: 10px;">
          Heavy-Duty Loco-Manipulation Tasks via <em>Teleoperation</em>
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        <p class="content is-medium" style="text-align: center; padding-bottom: 20px;">
          We compare the real-world performance of <strong style="color: #13c253; font-weight: 800;">FALCON</strong> with a <strong style="color: #e63946; font-weight: 800;">baseline</strong> that uses <em>RL for low-body locomotion and IK for upper-body manipulation w/o any force curriculum</em>, on the following three tasks: <b>Door-Opening</b>, <b>Cart-Pulling</b>, and <b>Heavy-Lifting</b>.
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Cart-Pulling (Baseline)</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Cart-Pulling (FALCON)</h3>
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                <h3 class="title is-3" style="margin-bottom: 10px; font-size: x-large;">Door-Opening (Baseline)</h3>
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                      src="./static/videos/g1_door_opening_baseline_unmask.mp4"
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                <h3 class="title is-3" style="margin-bottom: 10px; font-size: x-large;">Door-Opening (FALCON)</h3>
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                      src="./static/videos/g1_door_opening_falcon_unmask.mp4"
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Heavy-Lifting (Baseline)</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Heavy-Lifting (FALCON)</h3>
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          <em>Autonomous</em> Long-Horizon Forceful Loco-Manipulation Task
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        <h2 class="title is-2" style="text-align: center; padding-bottom: 10px;">
          More Forceful Loco-Manipulation Tasks via <em>Teleoperation</em>
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            <!-- <div class="columns">
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Door-Opening</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Cart-Pulling (Unitree G1)</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Cart-Pulling (Booster T1)</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Heavy-Lifting</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Adaptive Heavy-Lifting</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Stance-Pulling</h3>
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                <h3 class="title is-3" style="text-align: center; margin-bottom: 10px; font-size: x-large;">Tug-of-War: Round 1</h3>
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        <h2 class="title is-2">Abstract</h2>
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            Humanoid loco-manipulation holds transformative potential for daily service and industrial tasks, yet achieving precise, robust whole-body control with 3D end-effector force interaction remains a major challenge. Prior approaches are often limited to lightweight tasks or quadrupedal/wheeled platforms. To overcome these limitations, we propose <span class="method" style="color: darkred;">FALCON</span>, a dual-agent reinforcement-learning-based framework for robust force-adaptive humanoid loco-manipulation. <span class="method" style="color: darkred;">FALCON</span> decomposes whole-body control into two specialized agents: (1) a lower-body agent ensuring stable locomotion under external force disturbances, and (2) an upper-body agent precisely tracking end-effector positions with implicit adaptive force compensation. These two agents are jointly trained in simulation with a force curriculum that progressively escalates the magnitude of external force exerted on the end effector while respecting torque limits. Experiments demonstrate that, compared to the baselines, <span class="method" style="color: darkred;">FALCON</span> achieves 2$\times$ more accurate upper-body joint tracking, while maintaining robust locomotion under force disturbances and achieving faster training convergence. Moreover, <span class="method" style="color: darkred;">FALCON</span> enables policy training without embodiment-specific reward or curriculum tuning. Using the same training setup, we obtain policies that are deployed across multiple humanoids, enabling forceful loco-manipulation tasks such as transporting payloads (0-20N force), cart-pulling (0-100N), and door-opening (0-40N) in the real world.
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    <h2 class="title is-2" style="text-align: center;">Method</h2>
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          <p>
            <span class="method" style="color: darkred;">FALCON</span> is a dual-agent reinforcement learning framework that decouples locomotion and manipulation with separate rewards for efficient training. During learning, a torque-limit-aware 3D force curriculum applies progressively stronger external forces to the end-effectors, maximizing force adaptivity while ensuring safety. <span class="method" style="color: darkred;">FALCON</span> can be seamlessly trained and deployed on different humanoid robots without embodiment-specific reward or curriculum tuning, showing its cross-platform generalization capability.
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    <!-- <h2 class="title is-4" style="text-align: center;">Training Framework</h2> -->
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            There are two steps within the <span class="method" style="color: darkred;">FALCON</span> framework:
              <ol>
                <li><strong>Two-Agent Joint Training with Whole-Body Proprioception</strong>:  <br>Two agents, each assigned distinct sub-tasks, are jointly trained using shared whole-body proprioception signals. During training, randomized 3D external forces—bounded by the upper-body joint torque limits—are applied to the end-effectors to encourage robust coordination;</li>
                <li><strong>External Disturbance Handling via Force Perturbation</strong><br> The agents learn to maintain task performance under force perturbations, developing complementary behaviors to counteract disturbances and achieve stable loco-manipulation.</li>
              </ol> -->
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        <img src="./static/images/FALCON-Pipeline.png" alt="" style="width: 90%;" />
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    <section class="section" id="BibTeX">
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        <h2 class="titile">BibTeX</h2>
        <pre><code>@article{zhang2025falcon,
          title={FALCON: Learning Force-Adaptive Humanoid Loco-Manipulation},
          author={Zhang, Yuanhang and Yuan, Yifu and Gurunath, Prajwal and He, Tairan and Omidshafiei, Shayegan and Agha-mohammadi, Ali-akbar and Vazquez-Chanlatte, Marcell and Pedersen, Liam and Shi, Guanya},
          journal={arXiv preprint arXiv:2505.06776},
          year={2025}
        }</code></pre>
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