Authors:
Yoonkyu Hwang
and
Masato Ishikawa
Affiliation:
Graduate School of Engineering, Osaka University, Yamadaoka, Suita, Osaka, Japan
Keyword(s):
Locomotion, Generative Model, Topological Representation, Sequential Variational Inference, Hierarchical Networks, User-oriented Interface.
Abstract:
We propose novel generative locomotion models for snake robots. Locomotion researches have been relied on human experts with rich domain-knowledge and experience. Although recent data-driven approaches can achieve explict controllers to make robots move, results often do not show enough interpretability with respect to user-oriented interface. The proposed model focuses on interpretable locomotion generation to help intuitive locomotion planning by end-users. First, we introduce the topological shaping for time-series training data. This allows us to bound the data to specific region, which leads to training/inference simplification, intuitive visualization, and finally high generalization property for the proposed framework. Second, the dedicated hierarchical networks were designed to propagate complex contexts in the snake robot locomotion. The result shows that our generative locomotion models can be utilized as user-oriented interface for interpretable locomotion design.