人工身体知覚のための感覚運動学習
Sensorimotor learning for artificial body perception
ベイズ推定から深層学習まで、人工エージェントが自身の身体と外界を区別する自己知覚を獲得する手法を概説し、その可能性と課題を示した。
著者: German Diez-Valencia, Takuya Ohashi, Pablo Lanillos, Gordon Cheng
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Artificial self-perception is the machine ability to perceive its own body, i.e., the mastery of modal and intermodal contingencies of performing an action with a specific sensors/actuators body configuration. In other words, the spatio-temporal patterns that relate its sensors (e.g. visual, proprioceptive, tactile, etc.), its actions and its body latent variables are responsible of the distinction between its own body and the rest of the world. This paper describes some of the latest approaches for modelling artificial body self-perception: from Bayesian estimation to deep learning. Results show the potential of these free-model unsupervised or semi-supervised crossmodal/intermodal learning approaches. However, there are still challenges that should be overcome before we achieve artificial multisensory body perception.
