SA-Net：RGB-Dストリームからロボット軌道を認識する深層ニューラルネットワーク
SA-Net: Deep Neural Network for Robot Trajectory Recognition from RGB-D Streams
RGB-Dデータから状態-行動ペアを認識する深層学習モデルSA-Netを提案し、移動ロボットとマニピュレータの実機実験で従来手法より高精度な軌道認識を実現した。
著者: Nihal Soans, Ehsan Asali, Yi Hong, Prashant Doshi
分類: cs.RO
原文アブストラクト
Learning from demonstration (LfD) and imitation learning offer new paradigms for transferring task behavior to robots. A class of methods that enable such online learning require the robot to observe the task being performed and decompose the sensed streaming data into sequences of state-action pairs, which are then input to the methods. Thus, recognizing the state-action pairs correctly and quickly in sensed data is a crucial prerequisite for these methods. We present SA-Net a deep neural network architecture that recognizes state-action pairs from RGB-D data streams. SA-Net performed well in two diverse robotic applications of LfD -- one involving mobile ground robots and another involving a robotic manipulator -- which demonstrates that the architecture generalizes well to differing contexts. Comprehensive evaluations including deployment on a physical robot show that \sanet{} significantly improves on the accuracy of the previous method that utilizes traditional image processing and segmentation.
