将高频动作学习从动作空间转至隐空间，提升机器人控制的平滑性与一致性。
Learning High-Frequency Continuous Action Chunks in Latent Space
- 用变分自编码器将高频动作学习迁移到隐空间，增强时空一致性
- 在60Hz以上频率下，任务完成率提升且运动更流畅，减少停顿和抖动
- 适合需要高精度连续动作的机器人操控任务，如抓取、装配等
现代机器人策略越来越多依赖动作分块来执行复杂任务。尽管动作分块在中等动作频率下能提升时间一致性，但在更高频率（如60~100Hz）下则显得不足，导致动作既不连续也不稳定。本文提出将高频动作学习从动作空间转移到隐空间，使用变分自编码器（VAE）建模。该方法显著提升了高频率控制下的时间与空间一致性。为实现平滑实时执行，进一步引入Reuse-then-Refine策略，在异步推理下优化相邻动作块间的连续性。实验在三个真实世界接触密集型任务上验证，所提方法能持续完成任务，运动更平滑。代码与数据已公开于https://github.com/tars-robotics/RTR。
原文摘要 · Abstract (English)
Modern robotic policies increasingly rely on action chunking to execute complex tasks in the physical world. While action chunking improves temporal consistency at moderate action frequencies, it becomes insufficient when the action frequency is further increased (e.g., to 60~Hz). At such high frequencies, policies often fail to generate actions that are both temporally smooth and spatially consistent. We address this challenge by shifting high-frequency action learning from the action space to a latent space with variational autoencoder (VAE). This formulation significantly improves both temporal and spatial consistency of high-frequency control. To enable smooth real-time execution, we further introduce Reuse-then-Refine, a chunk-level refine strategy that improves continuity between adjacent action chunks under asynchronous inference. As a result, robots controlled by our policy can execute complex contact-rich tasks continuously, with less pauses and jerky motions. Experiments on three real-world contact-rich robotic tasks show that our approach consistently completes tasks with smooth motions. Our code and data are available at https://github.com/tars-robotics/RTR.
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