# 平滑性作为稳定人形机器人运动的约束

> 原文链接：https://robotics.hellobike.com/papers/2026-09-21-2609-24552
> 频道：papers | 更新日期：2026-09-21 | 标签：arxiv, cs.RO
> 核心摘要：针对人形机器人全身控制平滑性与任务响应冲突，DeCap以分组物理约束实现平滑控制，并在真机全身控制任务上验证。

# 平滑性作为稳定人形机器人运动的约束

::: v-pre
> 原文链接：[Smoothness as a Constraint for Stable Humanoid Locomotion](https://arxiv.org/abs/2609.24552)（arXiv 2609.24552 · 发布日期：2026-09-21）

**Smoothness as a Constraint for Stable Humanoid Locomotion**

针对人形机器人全身控制平滑性与任务响应冲突，DeCap以分组物理约束实现平滑控制，并在真机全身控制任务上验证。

## 核心要点

- 现实场景中的人形机器人需要既响应任务又物理平滑的全身控制策略。
- DeCap将全身平滑性解耦为上下身约束组，并以物理运动限制显式建模。
- 有界屏障惩罚在接近限制时主动生效，以改善可行边界附近的约束满足。
- 真机实验中，相比奖励式平滑策略，上身动作率降2.50倍、加速度降2.18倍。

**验证范围与局限**：验证范围为真实世界人形机器人全身控制任务，并展示固定平滑约束可跨多样地形迁移。

## 英文摘要 (Abstract)

Embodied AI systems, particularly humanoid robots deployed in real world scenarios require whole-body control policies that are both task-responsive and physically smooth. However, smoothness is not uniform across the body: lower body must remain sufficiently reactive, while the upper body must be tightly regulated to preserve stability. Existing reinforcement learning approaches typically impose smoothness through auxiliary terms in the reward function, which compete with task objectives, treating the body as uniform and provide no direct control over the physical quantities responsible for smooth behavior. We introduce DeCap (Decoupled Constraint-aware policy), a constrained reinforcement learning algorithm that decouples whole-body smoothness into separate upper- and lower-body constraint groups, each formulates smoothness as explicit constraints on physical motion limits. To improve constraint satisfaction near feasibility boundaries, DeCap incorporates a bounded barrier penalty that activates proactively as limits are approached and remains bounded at the constraint limit. On real-world humanoid whole-body control task, DeCap reduces upper-body action rate by 2.50x and acceleration by 2.18x relative to reward-based smoothness policies, while also improving lower-body smoothness and reducing transient motion. We demonstrate that a fixed set of smoothness constraints transfers across diverse terrains, alleviating the need of extensive reward tuning.

作者：Panchal, Utsav, Denis V. Kleyko, Unal Artan, Amy Loutfi
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## 原始参考来源 (Sources)

- [arXiv 2609.24552：Smoothness as a Constraint for Stable Humanoid Locomotion](https://arxiv.org/abs/2609.24552)