追跡と予測のための深層順・逆知覚モデル
Deep Forward and Inverse Perceptual Models for Tracking and Prediction
状態から高次元画像を生成する順モデルと画像から状態を推定する逆モデルを深層学習で構築し、ロボットの追跡・予測タスクに応用して有効性を示した論文。
著者: Alexander Lambert, Amirreza Shaban, Amit Raj, Zhen Liu, Byron Boots
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
