ReVoLT-website/index.html at master · ventusff/ReVoLT-website · GitHub

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          content="ReVoLT: Relational Reasoning and Voronoi Local graph planning for Target-driven navigation">
    <meta name="author" content="Junjia Liu,
                                 Jianfei Guo,
                                 Zehui Meng,
                                 Jingtao Xue,
                                 Zhuang Fu,
                                 Guangwu Liu">

    <title>ReVoLT: Relational Reasoning and Voronoi Local graph planning for Target-driven navigation</title>
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    <h2>ReVoLT: Relational Reasoning and Voronoi Local graph planning for Target-driven navigation</h2>
    <!-- <h2>IROS 2021</h2> -->
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    <p class="authors">
        <!-- <a href="">Junjia Liu*</a>, -->
        <a>Junjia Liu*</a>,
        <a>Jianfei Guo*</a>,
        <a>Zehui Meng</a>,</br>
        <a>Jingtao Xue</a>,
        <a>Zhuang Fu</a>,
        <a>Guangwu Liu</a>
    </p>
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        <a class="btn btn-primary disabled" href="">Code(Coming soon)</a>
        <a class="btn btn-primary disabled" href="">Paper</a>
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        <p>
            Embodied AI is an inevitable trend that emphasizes
            the interaction between intelligent entities and the real world,
            with broad applications in Robotics, especially target-driven
            navigation. This task requires the robot to find an object of a
            certain category efficiently in an unknown domestic environment.
            Recent works focus on exploiting layout relationships by graph
            neural networks (GNNs). However, most of them obtain robot
            actions directly from observations in an end-to-end manner via
            an incomplete relation graph, which are not interpretable and re-
            liable. We decouple this task and propose ReVoLT, a hierarchical
            framework: (a) an object detection visual frontend, (b) a high-
            level reasoner (infers object-level sub-goals), (c) an intermediate-
            level planner (computes spatial location sub-goals from object-
            level sub-goals), and (d) a low-level controller (executes actions),
            which operates with a multi-layer semantic-spatial topological
            graph. The reasoner uses multiform structured relations as
            priors, which are obtained from combinatorial relation extraction
            networks composed of unsupervised GraphSAGE, GCN and
            GraphRNN-based Region Rollout. The reasoner performs with
            Upper Confidence Bound for Tree (UCT) to select object-level
            sub-goals, accounting for tradeoffs between exploitation (depth-
            first searching) and exploration (regretting). The lightweight
            planner generates spontaneous spatial location sub-goals from
            object-level subgoals through an online constructed Voronoi local
            graph, replacing classical SLAM. The simulation experiments
            demonstrate that our framework achieves better performance
            in the target-driven navigation tasks and generalizes well, which
            is superior to the existing state-of-the-art methods.
        </p>
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            @inproceedings{sitzmann2019metasdf,
                author = {Sitzmann, Vincent
                          and Chan, Eric R.
                          and Tucker, Richard
                          and Snavely, Noah
                          and Wetzstein, Gordon},
                title = {MetaSDF: Meta-Learning Signed
                         Distance Functions},
                booktitle = {arXiv},
                year={2020}
            }
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