見守りロボット：忘れた行動を教師なし学習で指摘
Watch-Bot: Unsupervised Learning for Reminding Humans of Forgotten Actions
Kinectで人の行動を観察し、教師なし学習で行動と物体の共起や時間関係を学んで忘れた行動を検出し、レーザーポインタで関連物体を指し示して思い出させるロボットシステムを提案した。
著者: Chenxia Wu, Jiemi Zhang, Bart Selman, Silvio Savarese, Ashutosh Saxena
分類: cs.RO, cs.CV
原文アブストラクト
We present a robotic system that watches a human using a Kinect v2 RGB-D sensor, detects what he forgot to do while performing an activity, and if necessary reminds the person using a laser pointer to point out the related object. Our simple setup can be easily deployed on any assistive robot. Our approach is based on a learning algorithm trained in a purely unsupervised setting, which does not require any human annotations. This makes our approach scalable and applicable to variant scenarios. Our model learns the action/object co-occurrence and action temporal relations in the activity, and uses the learned rich relationships to infer the forgotten action and the related object. We show that our approach not only improves the unsupervised action segmentation and action cluster assignment performance, but also effectively detects the forgotten actions on a challenging human activity RGB-D video dataset. In robotic experiments, we show that our robot is able to remind people of forgotten actions successfully.
