生物に着想を得た可塑性ニューラルネットワークによる複雑な動物型ロボットのゼロショット分布外汎化
Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots
ヘブ則に基づく可塑性ニューラルネットワークに重み正規化を導入し、18自由度のフンコロガシ型と16自由度のヤモリ型ロボットの歩行制御において、ゼロショットでのsim-to-real適応や未見の不整地・形態損傷への汎化を実現した。
著者: Binggwong Leung, Worasuchad Haomachai, Joachim Winther Pedersen, Sebastian Risi, Poramate Manoonpong
分類: cs.RO, cs.AI
原文アブストラクト
Artificial neural networks can be used to solve a variety of robotic tasks. However, they risk failing catastrophically when faced with out-of-distribution (OOD) situations. Several approaches have employed a type of synaptic plasticity known as Hebbian learning that can dynamically adjust weights based on local neural activities. Research has shown that synaptic plasticity can make policies more robust and help them adapt to unforeseen changes in the environment. However, networks augmented with Hebbian learning can lead to weight divergence, resulting in network instability. Furthermore, such Hebbian networks have not yet been applied to solve legged locomotion in complex real robots with many degrees of freedom. In this work, we improve the Hebbian network with a weight normalization mechanism for preventing weight divergence, analyze the principal components of the Hebbian's weights, and perform a thorough evaluation of network performance in locomotion control for real 18-DOF dung beetle-like and 16-DOF gecko-like robots. We find that the Hebbian-based plastic network can execute zero-shot sim-to-real adaptation locomotion and generalize to unseen conditions, such as uneven terrain and morphological damage.
