让六自由度机械臂实时生成有表现力且防碰撞的舞蹈化动作
RMG: Real-Time Expressive Motion Generation with Self-collision Avoidance for 6-DOF Companion Robotic Arms
- 基于人类舞蹈数据构建表达性动作映射库
- 在0.5秒内生成符合约束的流畅动作，支持任意起止状态
- 适合人机交互场景，尤其需自然肢体表达的应用
六自由度（6-DOF）机械臂已广泛应用于人机共存环境。以往研究多聚焦于功能性运动生成，而人机交互中的表现性运动仍鲜有探索。本文提出一种实时运动生成规划方法，可在预设时间约束下，从任意起始状态到目标状态生成具有表现力的机械臂动作。主要贡献包括：首先，开发了一种映射算法，从人类舞蹈动作中构建表达性动作数据集；其次，使用该数据集在笛卡尔空间与关节空间分别训练运动生成模型；第三，引入优化算法，在保证动作流畅性和表达风格的同时，确保无自碰撞。实验表明，该方法可在0.5秒内生成满足所有约束的表达性通用动作。
原文摘要 · Abstract (English)
The six-degree-of-freedom (6-DOF) robotic arm has gained widespread application in human-coexisting environments. While previous research has predominantly focused on functional motion generation, the critical aspect of expressive motion in human-robot interaction remains largely unexplored. This paper presents a novel real-time motion generation planner that enhances interactivity by creating expressive robotic motions between arbitrary start and end states within predefined time constraints. Our approach involves three key contributions: first, we develop a mapping algorithm to construct an expressive motion dataset derived from human dance movements; second, we train motion generation models in both Cartesian and joint spaces using this dataset; third, we introduce an optimization algorithm that guarantees smooth, collision-free motion while maintaining the intended expressive style. Experimental results demonstrate the effectiveness of our method, which can generate expressive and generalized motions in under 0.5 seconds while satisfying all specified constraints.
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