# 导航中的近距离-安全平衡：面向行人群体中人类跟随的约束分解

> 原文链接：https://robotics.hellobike.com/papers/2026-08-10-2608-10056
> 频道：papers | 更新日期：2026-08-10 | 标签：arxiv, cs.RO
> 核心摘要：该方法将人类跟随分解为稀疏任务奖励与独立成本约束，并在分布内外实验和真机部署中验证。

# 导航中的近距离-安全平衡：面向行人群体中人类跟随的约束分解

::: v-pre
> 原文链接：[Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds](https://arxiv.org/abs/2608.10056)（arXiv 2608.10056 · 发布日期：2026-08-10）

**Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds**

该方法将人类跟随分解为稀疏任务奖励与独立成本约束，并在分布内外实验和真机部署中验证。

## 核心要点

- 拥挤环境中跟随目标人需兼顾贴近与避障，密集或不可预测行为下冲突更突出。
- 方法采用多约束强化学习，用具直接行为意义的成本阈值显式调节权衡。
- 进一步量化人类运动预测不确定性，并将其纳入强化学习成本以提升安全性。
- 分布内与分布外实验显示方法较基线取得有效平衡，真机部署验证可行性。

**验证范围与局限**：验证范围包括分布内外实验以及真实机器人在人类跟随场景中的部署。

## 英文摘要 (Abstract)

Following a target human in crowded environments involves an inherent conflict between staying close to the target and navigating safely among surrounding pedestrians and obstacles. This conflict becomes more severe in dense scenarios, where aggressive following risks collisions and conservative margins lead to target loss, especially when pedestrian behaviors are unfamiliar or unpredictable. Existing reinforcement learning (RL) methods typically encode these competing objectives into a single dense reward, but the resulting proximity-safety balance is implicit and difficult to adjust across conditions. To address this, we decompose the human-following task into a sparse task reward and independent cost constraints within a multi-constraint RL formulation, where each constraint is managed through cost thresholds with direct behavioral meaning rather than implicit reward weight ratios, allowing explicit and tunable control over the trade-off. We further quantify the prediction uncertainty of human motions and integrate these estimates into the RL costs to enhance safety under unpredictable conditions. Extensive experiments across both in-distribution and out-of-distribution settings demonstrate that our method achieves an effective proximity-safety balance compared to baselines. Real-robot deployment further validates the feasibility of our method in real-world scenarios. More details are available on our project page: https://nav-ps-balance.github.io/.

作者：Shiting Gong, Jianpeng Yao, Jinfeng Wang, Marco Pavone, Jiachen Li
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## 原始参考来源 (Sources)

- [arXiv 2608.10056：Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds](https://arxiv.org/abs/2608.10056)