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We introduce a novel formulation for incorporating visual feedback incontrolling robots. We define a generative model from actions to imageobservations of features on the end-effector. Inference in the model allows usto infer the robot state corresponding to target locations of the features.This, in turn, guides motion of the robot and allows for matching the targetlocations of the features in significantly fewer steps than state-of-the-artvisual servoing methods. The training procedure for our model enables effectivelearning 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 andshows strong generalization to test data. Since our formulation is modular, wecan modify components of our setup, like cameras and objects, and relearn themquickly online. Our method can handle noise in the observed state and noise inthe controllers that we interact with. We demonstrate the effectiveness of ourmethod by executing grasping and tight-fit insertions on robots with inaccuratecontrollers.

Learning a generative model for robot control using visual feedback