arXiv 2003.044742020-03-10cs.ROcs.AI

使用视觉反馈学习机器人控制的生成模型

Learning a generative model for robot control using visual feedback

Nishad Gothoskar, Miguel Lázaro-Gredilla, Abhishek Agarwal, Yasemin Bekiroglu, Dileep George

摘要：

我们介绍了一种新的配方，将视觉反馈控制机器人。我们定义了一个生成模型，从行动的图像观察功能的末端执行器。模型中的推理允许我们推断与特征的目标位置相对应的机器人状态。这反过来又引导机器人的运动，并允许以比最先进的视觉伺服方法少得多的步骤来匹配特征的目标位置。我们模型的训练过程可以同时有效地学习运动学，特征结构和相机参数。这可以在没有关于机器人、结构和观察它的摄像机的先验信息的情况下完成。学习是样本有效地完成的，并且对测试数据表现出很强的泛化能力。由于我们的公式是模块化的，我们可以修改设置的组件，如相机和对象，并在线快速重新学习它们。我们的方法可以处理观测状态中的噪声和与我们交互的控制器中的噪声。我们证明了我们的方法的有效性，通过执行把握和紧配合插入机器人不准确的控制器。

英文摘要：

We introduce a novel formulation for incorporating visual feedback in controlling robots. We define a generative model from actions to image observations of features on the end-effector. Inference in the model allows us to infer the robot state corresponding to target locations of the features. This, in turn, guides motion of the robot and allows for matching the target locations of the features in significantly fewer steps than state-of-the-art visual servoing methods. The training procedure for our model enables effective learning of the kinematics, feature structure, and camera parameters, simultaneously. This can be done with no prior information about the robot, structure, and cameras that observe it. Learning is done sample-efficiently and shows strong generalization to test data. Since our formulation is modular, we can modify components of our setup, like cameras and objects, and relearn them quickly online. Our method can handle noise in the observed state and noise in the controllers that we interact with. We demonstrate the effectiveness of our method by executing grasping and tight-fit insertions on robots with inaccurate controllers.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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