Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception
Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception
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DOI：
10.1109/icra48891.2023.10161431
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Cheng-Chun Hsu;Zhenyu Jiang;Yuke Zhu
中科院分区：
文献类型：
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作者：
Cheng-Chun Hsu;Zhenyu Jiang;Yuke Zhu
将物理世界虚拟化为虚拟模型是机器人在真实的世界中导航和规划的关键技术。为了在日常生活中促进对铰接对象的操作，这项工作探索通过机器人在这些场景中有目的的交互动作来构建室内场景的铰接模型。先前关于清晰度推理的工作主要集中在有限类别的孤立对象上。为了扩展到房间规模的环境中，机器人必须有效地探索大规模的3D空间，定位有关节的物体，并推断它们的关节。我们引入一个互动的感知方法来完成这项任务。我们的方法，命名为同上的房子，发现可能的关节对象，通过启示预测，与这些对象进行交互，以产生关节运动，并推断关节属性从视觉观察之前和之后的每一个互动。它紧密结合启示预测和清晰度推理，以改善这两项任务。我们证明了我们的方法在模拟和现实世界的场景中的有效性。代码和其他结果可在https://ut-austin-rpl.github.io/HouseDitto/上获取
Virtualizing the physical world into virtual models has been a critical technique for robot navigation and planning in the real world. To foster manipulation with articulated objects in everyday life, this work explores building articulation models of indoor scenes through a robot's purposeful inter-actions in these scenes. Prior work on articulation reasoning primarily focuses on siloed objects of limited categories. To extend to room-scale environments, the robot has to efficiently and effectively explore a large-scale 3D space, locate articulated objects, and infer their articulations. We introduce an interactive perception approach to this task. Our approach, named Ditto in the House, discovers possible articulated objects through affordance prediction, interacts with these objects to produce articulated motions, and infers the articulation properties from the visual observations before and after each interaction. It tightly couples affordance prediction and articulation inference to improve both tasks. We demonstrate the effectiveness of our approach in both simulation and real-world scenes. Code and additional results are available at https://ut-austin-rpl.github.io/HouseDitto/