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<h1 class="title is-1 publication-title">ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://zdchan.github.io/">Hui Zhang</a><sup>1,2*</sup>,</span>
<span class="author-block">
<a href="https://ait.ethz.ch/people/sammyc">Sammy Christen</a><sup>1*</sup>,</span>
<span class="author-block">
<a href="https://ait.ethz.ch/people/zfan">Zicong Fan</a><sup>1,2</sup>,</span>
</span>
<span class="author-block">
<a href="https://ait.ethz.ch">Luocheng Zheng</a><sup>1</sup>,</span>
</span>
<br>
<span class="author-block">
<a href="https://www.railab.kaist.ac.kr/sections/members">Jemin Hwangbo</a><sup>3</sup>,</span>
</span>
<span class="author-block">
<a href="https://ait.ethz.ch/people/song">Jie Song</a><sup>1</sup>,</span>
</span>
<span class="author-block">
<a href="https://ait.ethz.ch/people/hilliges">Otmar Hilliges</a><sup>1</sup>
</span>
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<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Department of Computer Science, ETH Zurich, Switzerland</span>
<br>
<span class="author-block"><sup>2</sup>Max Planck Institute for Intelligent Systems, Tübingen, Germany</span>
<br>
<span class="author-block"><sup>3</sup>Department of Mechanical Engineering, KAIST, Korea </span>
</div>

<div class="is-size-5">
*Equal Contribution
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<div class="is-size-5">
<span class="author-block"> <b>Accepted by 3DV 2024 as Spotlight Presentation</b></span>
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<h2 class="subtitle has-text-centered">
ArtiGrasp is a method to synthesize physically plausible bi-manual manipulation. It can generate motion sequences
such as grasping and relocating an object with one or two hands, and opening it to a target articulation angle.
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We present ArtiGrasp, a novel method to synthesize bi-manual hand-object interactions that include grasping and articulation. This task is challenging due to the diversity of the global wrist motions and the precise finger control that are necessary to articulate objects. ArtiGrasp leverages reinforcement learning and physics simulations to train a policy that controls the global and local hand pose. Our framework unifies grasping and articulation within a single policy guided by a single hand pose reference. Moreover, to facilitate the training of the precise finger control required for articulation, we present a learning curriculum with increasing difficulty. It starts with single-hand manipulation of stationary objects and continues with multi-agent training including both hands and non-stationary objects. To evaluate our method, we introduce Dynamic Object Grasping and Articulation, a task that involves bringing an object into a target articulated pose. This task requires grasping, relocation, and articulation. We show our method's efficacy towards this task. We further demonstrate that our method can generate motions with noisy hand-object pose estimates from an off-the-shelf image-based regressor.
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<h2 class="title">BibTeX</h2>
<pre><code>@inProceedings{zhang2024artigrasp,
title={{ArtiGrasp}: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation},
author={Zhang, Hui and Christen, Sammy and Fan, Zicong and Zheng, Luocheng and Hwangbo, Jemin and Song, Jie and Hilliges, Otmar},
booktitle={International Conference on 3D Vision (3DV)},
year={2024}
}</code></pre>
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