Deep Forward and Inverse Perceptual Models for Tracking and Prediction
2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA)(2018)
摘要
We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.
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关键词
high-dimensional images,deconvolutional methods,robotic system,robot trajectories,convolutional neural network model,photo-realistic images,image generation,video frames,perceptual model,robotics,inverse models,inverse perceptual models
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