用深度傅里叶模拟实现舞蹈动作自然衔接与多任务协同
DFM: Deep Fourier Mimic for Expressive Dance Motion Learning
- 将舞蹈动作建模为可学习的傅里叶系数，突破局部周期性限制
- 相比传统方法，动作追踪精度提升且过渡更平滑
- 支持跳舞时同步进行移动和眼神交流，适合交互式娱乐机器人
随着娱乐机器人日益普及，对自然且富有表现力的舞蹈动作需求持续增长。传统舞蹈动作由艺术家手工设计，过程耗时且仅能播放简单动作，难以融合行走或视线控制等附加任务。为此，我们提出深度傅里叶模仿（DFM），结合先进运动表征与强化学习（RL），在舞蹈序列中实现动作间平滑过渡，并同步处理辅助任务。尽管以往基于频域的运动表示能将舞蹈动作编码为潜在参数，但常在局部强加周期性假设，导致追踪精度下降、表现力不足。通过放松局部周期性约束，本方法不仅提升追踪精度，还促进不同动作间的流畅衔接。此外，所学强化学习策略支持同时执行基础任务如行走与视线控制，使娱乐机器人能更动态、互动地与用户交互，而非仅重复预设的静态舞步。
原文摘要 · Abstract (English)
As entertainment robots gain popularity, the demand for natural and expressive motion, particularly in dancing, continues to rise. Traditionally, dancing motions have been manually designed by artists, a process that is both labor-intensive and restricted to simple motion playback, lacking the flexibility to incorporate additional tasks such as locomotion or gaze control during dancing. To overcome these challenges, we introduce Deep Fourier Mimic (DFM), a novel method that combines advanced motion representation with Reinforcement Learning (RL) to enable smooth transitions between motions while concurrently managing auxiliary tasks during dance sequences. While previous frequency domain based motion representations have successfully encoded dance motions into latent parameters, they often impose overly rigid periodic assumptions at the local level, resulting in reduced tracking accuracy and motion expressiveness, which is a critical aspect for entertainment robots. By relaxing these locally periodic constraints, our approach not only enhances tracking precision but also facilitates smooth transitions between different motions. Furthermore, the learned RL policy that supports simultaneous base activities, such as locomotion and gaze control, allows entertainment robots to engage more dynamically and interactively with users rather than merely replaying static, pre-designed dance routines.
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