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        content="NESSIE : Navigation and Embodied Systems for
        Synthetic Integration and Enhancement">
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          <h1 class="title is-1 publication-title">NESSIE: Navigation and Embodied Systems for Synthetic Integration and Enhancement</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              <p>Vladislav Sarmatin,</p></span>
            <span class="author-block">
              <p>Javier Ramirez,</p></span>
            <span class="author-block">
              <p>Aleksandr Kashirin,</p></span>
            <span class="author-block">
              <p>Iaroslav Kolomiets,</p></span>
            <span class="author-block">
              <p>Jingtao Xu,</p></span>
            <span class="author-block">
              <p>Anastasija Cvijovic,</p></span>
            <span class="author-block">
              <p>Ruslan Babakyan,</p></span>
            <span class="author-block">
              <p>Timur Akhtyamov,</p></span>
            <span class="author-block">
              <p>Gonzalo Ferrer</p></span>
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        <h2 class="title is-3">NESSIE Overview</h2>
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          <p>
            Robotic manipulation datasets at scale remain concentrated
in controlled laboratory and household settings. Most feature
fixed lighting, low environmental variability, limited vertical
reach, and single-item tasks. Scenarios common in ware-
house and dark-store fulfillment—multi-height shelf access,
sequential retrieval, and manipulation of diverse objects in
constrained spaces—are largely absent. Open-source mobile
manipulators show similar gaps: no available platform com-
bines bimanual capability, vertical lift, teleoperation, and cost
feasibility for commercial deployment.
          </p>
          <p>
            This absence limits progress in a domain with high eco-
nomic impact and strong demand for automation. Fulfill-
ment robotics requires evaluation on tasks that reflect opera-
tional realities, not only curated tabletop benchmarks. Public,
application-driven datasets and platforms can enable more
generalizable policies and reduce the distance from research
results to deployed systems.
          </p>
          <p>
            Robots must coordinate two arms for complex actions,
operate in narrow aisles, adapt to variable lighting, and retrieve
items from shelves at multiple heights. Collecting diverse,
high-quality, long-horizon demonstrations under these con-
straints is costly, physically demanding, and often dependent
on proprietary hardware.
          </p>
          <p>
            We present NESSIE: a bimanual, vertically capable, master-
slave mobile manipulation platform and dataset designed for
dark-store scenarios. NESSIE contains 50 hours of application-
driven demonstrations in the LeRobot format, combining real-
world teleoperation with synthetically augmented trajectories via semantic inpainting and trajectory-level stitching. It ad-
dresses the missing task coverage in existing datasets and
provides an open, reproducible resource for data-efficient
learning in commercial fulfillment contexts.
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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{park2026nessie,
  author    = {Vladislav Sarmatin and Javier Ramirez and Aleksandr Kashirin and Iaroslav Kolomiets and Jingtao Xu and Anastasija Cvijovic and Ruslan Babakyan and Timur Akhtyamov and Gonzalo Ferrer},
  title     = {NESSIE: Navigation and Embodied Systems for Synthetic Integration and Enhancement},
  journal   = {},
  year      = {2026},
}</code></pre>
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