用动态运动基元实现机器人自然生动的交互动作生成
Interactive Expressive Motion Generation Using Dynamic Movement Primitives
- 基于动态运动基元构建可调节的动画原则模型
- 单个模型生成多样细腻的表达性动作，适配不同机器人
- 支持在线调整与组合，适合社交机器人实时交互
本文旨在让社交机器人能够以真实、吸引人且富有表现力的方式自主与人类互动。动画十二法则是一套被广泛认可的动画创作框架，帮助角色动作显得可信、生动且情感丰富。本文提出一种新方法，利用动态运动基元（DMPs）实现关键动画原则，构建了一个可学习、可解释、可模块化、可在线适应且可组合的自动表达性动作生成模型。DMPs最初用于机器人通用模仿学习，基于弹簧阻尼系统设计，具备数学优势，可调节各原则的强度，并将复杂表达性动作序列分解为可学习、可参数化的基元。我们给出了参数化动画原则的数学表达，并在三种具有不同运动学结构的机器人平台上——包括仿真环境、实际机器人和用户实验——验证了该框架的有效性。结果表明，仅用一个基础模型即可生成多样化且细腻的动作表达。
原文摘要 · Abstract (English)
Our goal is to enable social robots to interact autonomously with humans in a realistic, engaging, and expressive manner. The 12 Principles of Animation are a well-established framework animators use to create movements that make characters appear convincing, dynamic, and emotionally expressive. This paper proposes a novel approach that leverages Dynamic Movement Primitives (DMPs) to implement key animation principles, providing a learnable, explainable, modulable, online adaptable and composable model for automatic expressive motion generation. DMPs, originally developed for general imitation learning in robotics and grounded in a spring-damper system design, offer mathematical properties that make them particularly suitable for this task. Specifically, they enable modulation of the intensities of individual principles and facilitate the decomposition of complex, expressive motion sequences into learnable and parametrizable primitives. We present the mathematical formulation of the parameterized animation principles and demonstrate the effectiveness of our framework through experiments and application on three robotic platforms with different kinematic configurations, in simulation, on actual robots and in a user study. Our results show that the approach allows for creating diverse and nuanced expressions using a single base model.
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