# Chronos Policy：在物理时间上学习机器人策略

> 原文链接：https://robotics.hellobike.com/papers/2026-10-09-2610-13929
> 频道：papers | 更新日期：2026-10-09 | 标签：arxiv, cs.RO
> 核心摘要：Chronos Policy通过显式条件化观测与执行时间，解决时序变化下策略不稳，并在仿真和真实实验中验证。

# Chronos Policy：在物理时间上学习机器人策略

::: v-pre
> 原文链接：[Chronos Policy: Learning Robot Policies on Physical Time](https://arxiv.org/abs/2610.13929)（arXiv 2610.13929 · 发布日期：2026-10-09）

**Chronos Policy: Learning Robot Policies on Physical Time**

Chronos Policy通过显式条件化观测与执行时间，解决时序变化下策略不稳，并在仿真和真实实验中验证。

## 核心要点

- 固定频率观测-动作序列将物理时间隐含在位置中，难应对频率、延迟和推理时延变化。
- 方法以观测采集时间和期望动作执行时间为条件，并比较时间戳嵌入与time-aware RoPE。
- 训练中随机化观测采样和动作块起点，可覆盖更多部署时序并利用异步、多速率数据。
- 仿真和真实实验显示，其提升动态与多感知任务表现，并保持准静态任务标称性能与鲁棒性。

**验证范围与局限**：验证范围为仿真与真实世界实验中的动态、多感知和标准准静态任务。

## 英文摘要 (Abstract)

Robot policies often convert sensing and control into fixed-rate observation-action sequences, leaving physical time implicit in sequence position. This assumption breaks when observation rates or delays vary, or when inference latency changes. We introduce Chronos Policy, which makes physical time explicit by conditioning on observation capture times and desired action execution times. We study two ways to incorporate time into a diffusion transformer: adding timestamp embeddings to token features, or encoding physical time in positional encoding through time-aware rotary positional encodings (time-aware RoPE). We find that time-aware RoPE is more robust, allowing attention to depend directly on physical time while keeping token content unchanged. During training, we temporally randomize which observations are sampled and when the action chunk begins, exposing the policy to a wider range of sensing and execution schedules that may occur during deployments. This also enables learning from asynchronous, multi-rate data without forcing all measurements onto a fixed temporal grid. Across simulation and real-world experiments, Chronos Policy improves performance on dynamic tasks, where observation-to-action timing is critical, and on multisensory tasks, where the relative timing between observations matters. On standard quasi-static tasks, it preserves nominal performance while improving robustness to changes in sensing and inference timing.

作者：Changhao Wang, Jaden Clark, Shuran Song
:::

## 原始参考来源 (Sources)

- [arXiv 2610.13929：Chronos Policy: Learning Robot Policies on Physical Time](https://arxiv.org/abs/2610.13929)