予測符号化に基づく視覚運動学習のための深層動的ニューラルネットワーク
Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning
予測符号化の枠組みで視覚と固有感覚の動的パターンを知覚・予測し、意図推定も行う深層動的ニューラルネットを提案し、ロボットのジェスチャー模倣実験で検証した。
著者: Jungsik Hwang, Jinhyung Kim, Ahmadreza Ahmadi, Minkyu Choi, Jun Tani
分類: cs.AI, cs.LG, cs.RO, q-bio.NC
原文アブストラクト
This study presents a dynamic neural network model based on the predictive coding framework for perceiving and predicting the dynamic visuo-proprioceptive patterns. In our previous study [1], we have shown that the deep dynamic neural network model was able to coordinate visual perception and action generation in a seamless manner. In the current study, we extended the previous model under the predictive coding framework to endow the model with a capability of perceiving and predicting dynamic visuo-proprioceptive patterns as well as a capability of inferring intention behind the perceived visuomotor information through minimizing prediction error. A set of synthetic experiments were conducted in which a robot learned to imitate the gestures of another robot in a simulation environment. The experimental results showed that with given intention states, the model was able to mentally simulate the possible incoming dynamic visuo-proprioceptive patterns in a top-down process without the inputs from the external environment. Moreover, the results highlighted the role of minimizing prediction error in inferring underlying intention of the perceived visuo-proprioceptive patterns, supporting the predictive coding account of the mirror neuron systems. The results also revealed that minimizing prediction error in one modality induced the recall of the corresponding representation of another modality acquired during the consolidative learning of raw-level visuo-proprioceptive patterns.
