kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation
kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation
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DOI：
10.1109/lra.2021.3062315
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发表时间：
2021-04-01
影响因子：
5.2
通讯作者：
Tedrake, Russ
中科院分区：
文献类型：
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作者：
Gao, Wei;Tedrake, Russ
在这封信中，我们将探索可推广的，感知到行动的机器人操作，用于精确的，接触丰富的任务。特别是，我们贡献了一个框架，自动处理一类对象的闭环机器人操作，尽管潜在的看不见的对象实例和显着的类别内的形状，大小和外观的变化。先前的方法通常在实时6-DOF姿态估计器之上构建反馈回路。然而，从一个固定的几何模板表示一个对象的参数化变换不捕捉大的类别内形状变化。因此，我们采用[13]中提出的基于关键点的对象表示用于类别级拾取和放置，并将其扩展到具有接触丰富任务的闭环操作策略。我们首先用局部方向信息来增强关键点。使用定向关键点，我们提出了一种新的对象为中心的动作表示在调节这些定向关键点的线/角速度或力/扭矩。这种配方是令人惊讶的通用-我们证明，它可以完成接触丰富的操作任务，需要精度和灵巧的一类物体具有不同的形状，尺寸和外观，如钉孔插入钉和孔具有显着的形状变化和紧密的间隙。与建议的对象和动作表示，我们的框架也是不可知的机器人把握姿势和初始对象配置，使其灵活的集成和部署。视频演示、源代码和补充材料可在https://sites.google.com/view/kpam2/home上获得。
In this letter, we explore generalizable, perception-to-action robotic manipulation for precise, contact-rich tasks. In particular, we contribute a framework for closed-loop robotic manipulation that automatically handles a category of objects, despite potentially unseen object instances and significant intra-category variations in shape, size and appearance. Previous approaches typically build a feedback loop on top of a realtime 6-DOF pose estimator. However, representing an object with a parameterized transformation from a fixed geometric template does not capture large intra-category shape variation. Hence we adopt the keypoint-based object representation proposed in [13] for category-level pick-and-place, and extend it to closed-loop manipulation policies with contact-rich tasks. We first augment keypoints with local orientation information. Using the oriented keypoints, we propose a novel object-centric action representation in terms of regulating the linear/angular velocity or force/torque of these oriented keypoints. This formulation is surprisingly versatile - we demonstrate that it can accomplish contact-rich manipulation tasks that require precision and dexterity for a category of objects with different shapes, sizes and appearances, such as peg-hole insertion for pegs and holes with significant shape variation and tight clearance. With the proposed object and action representation, our framework is also agnostic to the robot grasp pose and initial object configuration, making it flexible for integration and deployment. Video demonstration, source code and supplemental materials are available on https://sites.google.com/view/kpam2/home.