roboculture/index.html at main · ac-rad/roboculture · GitHub

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          <h1 class="title is-1 publication-title">RoboCulture: A Robotics Platform for Automated Biological Experimentation</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <a href="">Kevin Angers</a><sup>1*</sup>,</span>
            <span class="author-block">
              <a href="">Kourosh Darvish</a><sup>1,2,4</sup>,</span>
            <span class="author-block">
              <a href="">Naruki Yoshikawa</a><sup>1,2</sup>,
            </span>
            <span class="author-block">
              <a href="">Sargol Okhovatian</a><sup>1,3</sup>,
            </span>
            <span class="author-block">
              <a href="">Dawn Bannerman</a><sup>1,3</sup>,
            </span>
            <span class="author-block">
              <a href="">Ilya Yakavets</a><sup>1,4</sup>,
            </span>
            <span class="author-block">
              <a href="">Florian Shkurti</a><sup>1,2,4*†</sup>
            </span>
            <span class="author-block">
              <a href="">Alán Aspuru-Guzik</a><sup>1,2,4,5,6*</sup>
            </span>
            <span class="author-block">
              <a href="">Milica Radisic</a><sup>1,3,4*</sup>
            </span>
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          <div class="is-size-6 publication-authors">
            <span class="author-block"><sup>1</sup>University of Toronto,</span>
            <span class="author-block"><sup>2</sup>Vector Institute</span>
            <span class="author-block"><sup>3</sup>Toronto General Health Research Institute</span>
            <span class="author-block"><sup>4</sup>Acceleration Consortium</span>
            <span class="author-block"><sup>5</sup>Canadian Institute for Advanced Research</span>
            <span class="author-block"><sup>6</sup>NVIDIA</span>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Automating biological experimentation remains challenging due to the need for millimeter-scale precision, long and multi-step experiments, and the dynamic nature of living systems. Current liquid handlers only partially automate workflows, requiring human intervention for plate loading, tip replacement, and calibration. Industrial solutions offer more automation but are costly and lack the flexibility needed in research settings. Meanwhile, research in autonomous robotics has yet to bridge the gap for long-duration, failure-sensitive biological experiments.
          </p>
          <p>
            We introduce RoboCulture, a cost-effective and flexible platform that uses a general-purpose robotic manipulator to automate key biological tasks. RoboCulture performs liquid handling, interacts with lab equipment, and leverages computer vision for real-time decisions using optical density-based growth monitoring. We demonstrate a fully autonomous 15-hour yeast culture experiment where RoboCulture uses vision and force feedback and a modular behavior tree framework to robustly execute, monitor, and manage experiments.
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                  type="video/mp4">
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        <p>
          RoboCulture performing cell expansion (30x speed). After completing the optical density monitoring procedure, the robot initiates the well-splitting process. It first retrieves the pipette from its stand and attaches a fresh pipette tip. Growth media is then aspirated and dispensed into 18 empty wells. The robot proceeds by resuspending the first saturated well and distributing its contents into three of the pre-filled wells, followed by the disposal of the used pipette tip. For each of the remaining five saturated wells, a new pipette tip is attached, used, and discarded in sequence.
        </p>
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    <h3 class="title is-4">Pipette Tip Attachment and Dettachment</h3>
    <div class="columns is-centered">

      <!-- Pipette Tip Attachment -->
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        <div class="content">
          <!-- <h2 class="title is-3">Pipette Tip Attachment</h2> -->
          <p>
            RoboCulture cleverly attaches new pipette tips using a spiral motion and force feedback.
          </p>
          <video id="S2" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/S2.mp4"
                    type="video/mp4">
          </video>
        </div>
      </div>
      <!--/ Pipette Tip Attachment -->

      <!-- Pipette Tip Dettachment -->
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          <!-- <h2 class="title is-3">Pipette Tip Dettachment</h2> -->
          <p>
            The contaminated pipette is removed using a 3D-printed pipette tip remover.
          </p>
          <video id="S3" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/S3.mp4"
                    type="video/mp4">
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        </div>
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    <h3 class="title is-4">Visual Feedback for Liquid Handling</h3>
    <div class="columns is-centered">

      <!-- Pipette Tip Attachment -->
      <div class="column">
        <div class="content">
          <!-- <h2 class="title is-3">Visual Effects</h2> -->
          <p>
            Using a camera mounted on the robot, RoboCulture perceives the positions of the wells and the pipette tip. This video shows the perception output while moving the robot arm manually.
          </p>
          <div style="position: relative; width: 100%; padding-top: 56.25%; overflow: hidden;">
            <video
              id="S5"
              autoplay
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              loop
              playsinline
              style="position: absolute; top: 50%; left: 50%; transform: translate(-50%, -50%); min-width: 100%; min-height: 100%;">
              <source src="./static/videos/S5.mp4" type="video/mp4">
            </video>
          </div>
        </div>
      </div>
      <!--/ Pipette Tip Attachment -->

      <!-- Pipette Tip Dettachment -->
      <div class="column">
        <div class="content">
          <!-- <h2 class="title is-3">Visual Effects</h2> -->
          <p>
            Even under disturbances, RoboCulture can accurately detect the position of the wells and reposition the pipette tip accordingly.
          </p>
          <video id="S6" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/S6.mp4"
                    type="video/mp4">
          </video>
        </div>
      </div>
    </div>


    <h3 class="title is-4">Optical Density Perception</h3>
    <div class="columns is-centered">

      <!-- Pipette Tip Attachment -->
      <div class="column">
        <div class="content">
          <!-- <h2 class="title is-3">Visual Effects</h2> -->
          <p>
            RoboCulture also uses its camera to monitor the growth of the yeast culture and make experiment decisions based on the optical density of the wells. The robot pans across the well plate to capture images of the wells at regular intervals.
          </p>
          <video id="S7" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/S7.mp4"
                    type="video/mp4">
          </video>
        </div>
      </div>
      <!--/ Pipette Tip Attachment -->

      <!-- Pipette Tip Dettachment -->
      <div class="column">
        <div class="content">
          <!-- <h2 class="title is-3">Visual Effects</h2> -->
          <p>
            By imaging the wells at multiple intervals, RoboCulture constructs a growth curve for each well, informing its decision to split the wells.
          </p>
          <video id="S13" autoplay controls muted loop playsinline height="100%">
            <source src="./static/videos/S13.mp4"
                    type="video/mp4">
          </video>
        </div>
      </div>
    </div>

    <div class="mt-6"></div>

    <h3 class="title is-4">Well Passaging Close Up</h3>
    <video id="S11" autoplay controls muted loop playsinline height="50%">
      <source src="./static/videos/S11.mp4"
              type="video/mp4">
    </video>

    <!--/ Pipette Tip Dettachment -->

  </div>
</div>
<!--/ Pipette Tip Dettachment -->
</div>

    <!-- Animation. -->
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        <!-- <h2 class="title is-3">Animation</h2> -->

        <!-- Interpolating. -->
        <!-- <h3 class="title is-4">Interpolating states</h3>
        <div class="content has-text-justified">
          <p>
            We can also animate the scene by interpolating the deformation latent codes of two input
            frames. Use the slider here to linearly interpolate between the left frame and the right
            frame.
          </p>
        </div>
        <div class="columns is-vcentered interpolation-panel">
          <div class="column is-3 has-text-centered">
            <img src="./static/images/interpolate_start.jpg"
                 class="interpolation-image"
                 alt="Interpolate start reference image."/>
            <p>Start Frame</p>
          </div>
          <div class="column interpolation-video-column">
            <div id="interpolation-image-wrapper">
              Loading...
            </div>
            <input class="slider is-fullwidth is-large is-info"
                   id="interpolation-slider"
                   step="1" min="0" max="100" value="0" type="range">
          </div>
          <div class="column is-3 has-text-centered">
            <img src="./static/images/interpolate_end.jpg"
                 class="interpolation-image"
                 alt="Interpolation end reference image."/>
            <p class="is-bold">End Frame</p>
          </div>
        </div>
        <br/> -->
        <!--/ Interpolating. -->

        <!-- Re-rendering. -->
        <!-- <h3 class="title is-4">Re-rendering the input video</h3>
        <div class="content has-text-justified">
          <p>
            Using <span class="dnerf">Nerfies</span>, you can re-render a video from a novel
            viewpoint such as a stabilized camera by playing back the training deformations.
          </p>
        </div>
        <div class="content has-text-centered">
          <video id="replay-video"
                 controls
                 muted
                 preload
                 playsinline
                 width="75%">
            <source src="./static/videos/replay.mp4"
                    type="video/mp4">
          </video>
        </div> -->
        <!--/ Re-rendering. -->

      </div>
    </div>
    <!--/ Animation. -->


    <!-- Concurrent Work. -->
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      <div class="column is-full-width">
        <h2 class="title is-3">Related Links</h2>

        <div class="content has-text-justified">
          <p>
            There's a lot of excellent work that was introduced around the same time as ours.
          </p>
          <p>
            <a href="https://arxiv.org/abs/2104.09125">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
          </p>
          <p>
            <a href="https://www.albertpumarola.com/research/D-NeRF/index.html">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/">NR-NeRF</a>
            both use deformation fields to model non-rigid scenes.
          </p>
          <p>
            Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/">NSFF</a>, and <a href="https://neural-3d-video.github.io/">DyNeRF</a>
          </p>
          <p>
            There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF">Yen-Chen Lin's curated list of NeRF papers</a>.
          </p>
        </div>
      </div>
    </div> -->
    <!--/ Concurrent Work. -->

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<section class="section" id="BibTeX">
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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{angers2025roboculture,
      title={RoboCulture: A Robotics Platform for Automated Biological Experimentation},
      author={Kevin Angers and Kourosh Darvish and Naruki Yoshikawa and Sargol Okhovatian and Dawn Bannerman and Ilya Yakavets and Florian Shkurti and Alán Aspuru-Guzik and Milica Radisic},
      year={2025},
      eprint={2505.14941},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2505.14941},
}</code></pre>
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</section>


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