AdaptiveMPC/index.html at main · StochLab/AdaptiveMPC · GitHub

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<!DOCTYPE html>
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<head>
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  <meta name="keywords" content="Adaptive control, Legged Robots, MPC, Quadruped Robot, System identification">
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  <section class="hero">
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        <div class="column has-text-centered">
          <h1 class="title is-2 publication-title">Adaptive control of Quadruped robot under varying load conditions</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="https://scholar.google.com/citations?user=tHjGrMoAAAAJ&hl=en">Vamshi Kumar Kurva</a>,
            </span>
            <span class="author-block">
              <a href="https://www.shishirny.com/">Shishir Kolathaya</a>
            </span>
          </div>

          <div class="is-size-5 publication-authors">
            <span class="author-block"> Indian Institute of Science (IISc), Bangalore </span>
          </div>

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               <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10883701"
                  class="external-link button is-normal is-rounded is-dark">
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                 <span>Paper</span>
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</section>


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      <!-- Abstract. -->
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          <h2 class="title is-3">Abstract</h2>
          <div class="content has-text-justified">
            <p>
              Control frameworks for legged robots often rely
on accurate dynamic models. However, these models often
proves to be inaccurate due to factors such as mechanical
wear and tear, and unforeseen changes such as the addition
of extra payloads during deployment. Significant deviations in
the dynamics can severely impact the controller’s performance.
Our goal is to enhance the controller’s model in real-time
during deployment using onboard sensors and online learning.
Specifically, our work focuses on quadruped locomotion under
varying load conditions. This paper presents an adaptive force
control framework for quadruped robots, enhanced with online
system identification, to handle significant changes in both
mass and center of mass (CoM). The proposed approach
demonstrates superior velocity and height tracking, even under
extreme load conditions, showing promise for applications in
logistics, military, and rescue missions.
            </p>
            <br>
          </div>
        </div>
      </div>
    </div>
  </div>
</section>

<section class="hero">
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            <h2 class="title is-3"> Background </h2>
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          <p>
            Many controllers for legged locomotion still rely on an accurate robot model, especially Model Predictive Control (MPC), which assumes known dynamics and re-plans at every step. However, model inaccuracies due to wear, maintenance, and repairs can degrade performance. While some MPC-based methods in the literature have been modified for load-bearing tasks, they only handle minimal center-of-mass (CoM) shifts. Large CoM changes pose significant challenges, often leading to controller failure. The goal is to develop a controller that can adapt to substantial CoM variations, benefiting applications in logistics, search and rescue, military, and agriculture.
          </p>
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            <h2 class="title is-3"> Why is Payload Handling Challenging? </h2>
          </div>
      </div>
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          <div class="content has-text-justified" style="padding-top: 3%">
            <p>
              Handling payload is challenging because adding or removing weight shifts the center of mass (CoM) away from the torso’s geometric center. This shift creates an imbalance, generating a tipping moment about the diagonal axis connecting the diagonal foot positions. If not properly accounted for, this can cause the robot to lose stability and tip over during locomotion.
            </p>
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            <img src="images/effect_of_com_offset_big.png" width="50%">
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          <h2 class="title is-3"> Proposed Control Architecture </h2>
        </div>
      </div>
      <div class="columns is-centered">
        <div class="column">
          <div class="content has-text-justified" style="padding-top: 3%">
            <p>
              The control architecture comprises several modules:
              ConvexMPC, PD controller, and Online SysID. We employ ConvexMPC
              as a high-level controller to track the command velocities provided by
              a joystick. The high-level controller uses a gait scheduler that
              defines the gait timing and contact sequence for each leg. It
              then determines the desired foot forces for the stance leg and
              foot positions for the swing leg based on user commands.
              These high-level commands are converted into torques by
              the low-level controller. For the swing legs, foot positions
              are tracked using PD control, while for the stance legs,
              desired torques are generated using the leg Jacobian, which
              relates joint torques to end-effector forces. Online SysID is
              employed to identify changing parameters of interest using
              past data from the controller.
            </p>
          </div>
          <div class="has-text-centered" style="padding-top: 3%">
            <img src="images/AdapControlNew.png" alt="PIP-Loco Training Architecture" width="70%">
          </div>
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  </div>
</section>


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          <h2 class="title is-3"> Parameter estimation </h2>
        </div>
      </div>
      <div class="columns is-centered has-text-centered">
        <div class="content has-text-justified" style="padding-top: 3%">
          <p>
            The dynamics of the robot are influenced by system parameters such as mass,
            Center of Mass (CoM), inertia, and foot positions relative to the CoM.
            Accurately identifying these varying system parameters enhances the controller’s
            ability to effectively track commands. We propose the following two algorithms
            to estimate mass and CoM shift.
          </p>
      </div>
    </div>
    <div class="column has-text-centered">
      <div class="columns is-centered has-text-centered">
        <div class="column">
            <h3 class="title is-4"> </h3>
            <img src="images/mass_estimation.png" width="100%">
        </div>
        <div class="column">
            <h3 class="title is-4"> </h3>
            <img src="images/com_shift_estimation.png" width="100%">
        </div>
      </div>
      <div class="content has-text-justified" style="padding-top: 3%">
        <p>
          The mass estimation method calculates the robot’s mass by dividing the z-direction force
          acting on the robot by its z-direction acceleration at each time-step. CoM shift identifies
          specific torso points where moments about the x and y axes are zero, assuming negligible GRFs
          in the x and y directions compared to the z-direction. These individual estimates are averaged to obtain
          a reliable estimate.
        </p>
    </div>
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</section>

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            <h2 class="title is-3"> Results </h2>
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          <div class="content has-text-justified">
            <p>
              To validate our approach, we used the PyBullet simulator with a custom Stoch3 URDF model.
              The simulation provides full state access, including joint positions and velocities.
              ConvexMPC, running at 250 Hz, serves as the base controller, while the PD controller runs
              at 1 kHz. The robot maintains a trot gait with a desired body height of 0.42 meters.
              We initially placed a payload of 2 kg at a distance of (0.25, 0.17) meters from the center
              of the base frame. The payload mass was then incrementally increased until it reached 18 kg,
              followed by a similar decrement back to 2 kg. Consequently, the CoM shift varied correspondingly,
              reaching its maximum when the payload mass was highest and its minimum when the payload mass was lowest.
            </p>
          </div>
        </div>
      </div>

      <!-- Step in Place-->
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          <h4 class="title is-5">Step in Place</h4>
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        <div class="column">
          <video width="100%" style="padding: 2%;" autoplay loop muted playsinline>
            <source src="videos/no_sys_id_step_in_place.mp4" type="video/mp4">
            Your browser does not support the video tag.
          </video>
          <p class="has-text-centered">Baseline: No SysID</p>
        </div>
        <div class="column">
          <video width="100%" style="padding: 2%;" autoplay loop muted playsinline>
            <source src="videos/sys_id_step_in_place.mp4" type="video/mp4">
            Your browser does not support the video tag.
          </video>
          <p class="has-text-centered">Our Method: With SysID</p>
        </div>
      </div>

      <!-- Walking Forward-->
      <div class="columns is-centered has-text-centered">
        <div class="column">
          <h4 class="title is-5">Walking Forward</h4>
        </div>
      </div>
      <div class="columns is-centered">
        <div class="column">
          <video width="100%" style="padding: 2%;" autoplay loop muted playsinline>
            <source src="videos/no_sys_id_walk_forward.mp4" type="video/mp4">
            Your browser does not support the video tag.
          </video>
          <p class="has-text-centered">Baseline: No SysID</p>
        </div>
        <div class="column">
          <video width="100%" style="padding: 2%;" autoplay loop muted playsinline>
            <source src="videos/sys_id_walk_forward.mp4" type="video/mp4">
            Your browser does not support the video tag.
          </video>
          <p class="has-text-centered">Our Method: With SysID</p>
        </div>
      </div>

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        <div class="column">
          <div class="content has-text-justified">
            <p>
              The base controller struggles to
              track velocity commands, causing the robot to drift toward
              the CoM, and height tracking deteriorates with increasing
              payload mass. However, by identifying the changing parameters,
              our adaptive controller successfully tracks both height
              and velocity commands.
            </p>
          </div>
        </div>
      </div>

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          <h2 class="title is-3"> Conclusion </h2>
        </div>
      </div>
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        <div class="column">
          <div class="content has-text-justified" style="padding-top: 3%">
            <p>
              In this work, we presented a force-based controller capable
of adapting to changes in load conditions. We employed
a system identification technique to identify changing pa-
rameters of interest using data collected from the base con-
troller in real-time. We validated our controller in PyBullet
simulator under two different test conditions, demonstrating
superior command tracking compared to the base controller.
For future work, we plan to explore reinforcement learning-
based methods to adapt to various other uncertainties and
unstructured terrains
            </p>
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