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Title: Dex-Net AR: Distributed Deep Grasp Planning Using a Commodity Cellphone and Augmented Reality App
Consumer demand for augmented reality (AR) in mobile phone applications, such as the Apple ARKit. Such applications have potential to expand access to robot grasp planning systems such as Dex-Net. AR apps use structure from motion methods to compute a point cloud from a sequence of RGB images taken by the camera as it is moved around an object. However, the resulting point clouds are often noisy due to estimation errors. We present a distributed pipeline, DexNet AR, that allows point clouds to be uploaded to a server in our lab, cleaned, and evaluated by Dex-Net grasp planner to generate a grasp axis that is returned and displayed as an overlay on the object. We implement Dex-Net AR using the iPhone and ARKit and compare results with those generated with high-performance depth sensors. The success rates with AR on harder adversarial objects are higher than traditional depth images.
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Award ID(s):
Publication Date:
Journal Name:
2020 IEEE International Conference on Robotics and Automation (ICRA)
Page Range or eLocation-ID:
552 to 558
Sponsoring Org:
National Science Foundation
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