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Title: Keypoints-Based Adaptive Visual Servoing for Control of Robotic Manipulators in Configuration Space
This paper presents a visual servoing method for controlling a robot in the configuration space by purely using its natural features. We first created a data collection pipeline that uses camera intrinsics, extrinsics, and forward kinematics to generate 2D projections of a robot's joint locations (keypoints) in image space. Using this pipeline, we are able to collect large sets of real-robot data, which we use to train realtime keypoint detectors. The inferred keypoints from the trained model are used as control features in an adaptive visual servoing scheme that estimates, in runtime, the Jacobian relating the changes of the keypoints and joint velocities. We compared the 2D configuration control performance of this method to the skeleton-based visual servoing method (the only other algorithm for purely vision-based configuration space visual servoing), and demonstrated that the keypoints provide more robust and less noisy features, which result in better transient response. We also demonstrate the first vision-based 3D configuration space control results in the literature, and discuss its limitations. Our data collection pipeline is available at which can be utilized to collect image datasets and train realtime keypoint detectors for various robots and environments.  more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Date Published:
Journal Name:
Proceedings of 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Page Range / eLocation ID:
6387 to 6394
Subject(s) / Keyword(s):
["Keypoint tracking, vision-based control, visual servoing"]
Medium: X
Detroit, MI, USA
Sponsoring Org:
National Science Foundation
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