拡散モデルと不確実性を考慮した最適化によるタコの這行運動の多様で適応的な腕協調
Diverse and Adaptable Arm Coordination for Octopus-Crawling via Diffusion-Based Uncertainty-Aware Optimization
拡散ベースの不確実性考慮最適化(DUO)を提案し、筋骨格駆動のシミュレーションタコにおいて、多様な協調モードを学習して動的な物理制約に適応する這行制御を実現した。
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著者: Seung Hyun Kim, Heng-Sheng Chang, Kimia Kazemi, Prashant Mehta, Mattia Gazzola
分類: cs.RO, cs.LG, math.OC
原文アブストラクト
Octopus crawling motivates soft robots that exploit redundancy, yet discovering and organizing diverse coordination modes for adaptation remains challenging. To address this, we introduce a Diffusion-based Uncertainty-aware Optimization (DUO) algorithm that learns demonstration-free crawling controllers for a simulated, muscle-actuated CyberOctopus. This work represents the first application of diffusion-based control to soft multi-arm robots in contact-rich simulations. By embedding a variety of locomotion behaviors within a shared control distribution, this approach enables the simulated octopus to navigate dynamic physical constraints, demonstrating that learned coordination diversity inherently facilitates robust adaptation. The main contributions include: (i) a symmetry-structured policy representation that folds radially equivalent controllers into a canonical directional sector, (ii) an online black-box optimization strategy, the DUO algorithm, that discovers and retains diverse coordination modes, and (iii) a control editing technique that adapts existing controllers to novel actuator constraints without retraining. These results show how learned coordination diversity makes motor abundance a practical resource for adaptation in soft multi-arm robots.