发表机构
Department of Applied Informatics Comenius University Bratislava（布拉迪斯拉发夸美纽斯大学应用信息学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
受镜像神经元系统启发，提出两级架构用于人形机器人运动原语发现和在线相位识别。第一级用自组织映射学习运动学表示，第二级用回声状态网络评估激活轨迹。集成SOM和ESN方法，实现运动原语表示及相位识别，验证相关计算假设。
AI 中文摘要
理解动作识别的计算基础是社会认知以及人机交互中的核心挑战。受镜像神经元系统（MNS）启发，我们为应用于NICO人形机器人的运动原语发现和在线相位识别提出了一种两级架构。第一级，两个自组织映射（SOM）从涵盖七个运动动作的模拟试验中学习手臂运动学（A - SOM）和手部运动学（H - SOM）的地形表示，通过运动轨迹的分层相关分析确定非冗余特征进行训练，结果表明两个SOM编码了运动行为的互补方面。第二级，回声状态网络（ESN）评估由连续最佳匹配单元表示的SOM激活的时间轨迹是否足以在线识别当前执行的运动阶段，结果表明基于SOM的轨迹保留了运动的主要相位判别结构，而上下文信息仅提供次要细化。我们的贡献是在受MNS启发的架构中集成已有的SOM和ESN方法用于运动原语表示和在线相位识别，结果与自组织运动表示在时间上整合可支持准确在线识别正在进行的运动阶段的计算假设兼容。
英文摘要
Understanding the computational basis of action recognition is a central challenge in social cognition as well as in human-robot interaction. Inspired by the Mirror Neuron System (MNS), we propose a two-level architecture for motor primitive discovery and online phase recognition applied to the NICO humanoid robot. At the first level, two Self-Organising Maps (SOMs) learn topographic representations of arm kinematics (A-SOM) and hand kinematics (H-SOM) from simulated trials covering seven motor actions. The maps are trained on non-redundant features identified through hierarchical correlation analysis of motion trajectories. The results show that the two SOMs encode complementary aspects of motor behaviour. At the second level, an Echo State Network (ESN) evaluates whether temporal trajectories of SOM activations, represented by consecutive best-matching units, are sufficient for online recognition of the currently executed movement phase. The results show that SOM-based trajectories preserve the dominant phase-discriminative structure of the movement, while contextual information provides only a secondary refinement. Our contribution is the integration of established SOM and ESN methods within an MNS-inspired architecture for motor primitive representation and online phase recognition. The results are compatible with the computational hypothesis that self-organised motor representations, when temporally integrated, can support accurate online recognition of ongoing movement phases.
Comments12 pages, 4 figures
