Reinforcement-Learning Based Robotic Assembly of Fractured Objects Using Visual and Tactile Information
Reinforcement-Learning Based Robotic Assembly of Fractured Objects Using Visual and Tactile Information
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DOI：
10.1109/icara56516.2023.10125938
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Xinchao Song;N. Lamb;Sean Banerjee;N. Banerjee
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文献类型：
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作者：
Xinchao Song;N. Lamb;Sean Banerjee;N. Banerjee
虽然存在几种方法来自动生成断裂对象的修复部件，但很少有先前的工作自动组装所生成的修复部件。由于断裂区域复杂的高频几何形状，传统控制器的有效性受到限制，因此将修复部件组装到断裂对象是一个具有挑战性的问题。我们提出了一种使用强化学习的方法，该方法结合了视觉和触觉信息，可以自动将修复部件组装到断裂的物体上。我们的方法克服了现有组装方法的局限性，这些方法需要对象具有特定的结构，需要在大型数据集上进行训练以推广到新对象，或者需要组装状态易于识别，例如用于钉孔组装。我们提出了两个视觉指标，提供3自由度的装配状态估计。触觉信息允许我们的方法来组装对象的遮挡下，发生时，对象几乎组装。我们的方法是能够组装对象与复杂的接口，而不把对象结构的要求。
Though several approaches exist to automatically generate repair parts for fractured objects, there has been little prior work on the automatic assembly of generated repair parts. Assembly of repair parts to fractured objects is a challenging problem due to the complex high-frequency geometry at the fractured region, which limits the effectiveness of traditional controllers. We present an approach using reinforcement learning that combines visual and tactile information to automatically assemble repair parts to fractured objects. Our approach overcomes the limitations of existing assembly approaches that require objects to have a specific structure, that require training on a large dataset to generalize to new objects, or that require the assembled state to be easily identifiable, such as for peg-in-hole assembly. We propose two visual metrics that provide estimation of assembly state with 3 degrees of freedom. Tactile information allows our approach to assemble objects under occlusion, as occurs when the objects are nearly assembled. Our approach is able to assemble objects with complex interfaces without placing requirements on object structure.