GANを用いたロボットの半教師あり触覚材料認識
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks
力・温度・振動の触覚特徴とGANによる半教師あり学習で家庭用品の材質を約90%の精度で認識し、データセットも公開した。
著者: Zackory Erickson, Sonia Chernova, Charles C. Kemp
分類: cs.RO, cs.LG, stat.ML
原文アブストラクト
Material recognition enables robots to incorporate knowledge of material properties into their interactions with everyday objects. For example, material recognition opens up opportunities for clearer communication with a robot, such as "bring me the metal coffee mug", and recognizing plastic versus metal is crucial when using a microwave or oven. However, collecting labeled training data with a robot is often more difficult than unlabeled data. We present a semi-supervised learning approach for material recognition that uses generative adversarial networks (GANs) with haptic features such as force, temperature, and vibration. Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled. We explore how well this approach can recognize the material of new objects and we discuss challenges facing generalization. To motivate learning from unlabeled training data, we also compare results against several common supervised learning classifiers. In addition, we have released the dataset used for this work which consists of time-series haptic measurements from a robot that conducted thousands of interactions with 72 household objects.
