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GRIP: Generative Robust Inference and Perception for Semantic Robot Manipulation in Adversarial Environments
In this work, we propose Generative Robust Inference and Perception (GRIP) as a two-stage object detection and pose estimation system that aims to combine relative strengths of discriminative CNNs and generative inference methods to achieve robust estimation.
Plenoptic Monte Carlo Object Localization for Robot Grasping under Layered Translucency
In order to fully function in human environments, robot perception will need to account for the uncertainty caused by translucent materials.
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