# 面向人机协作的基于人类反馈的强化学习的范围综述与实验研究

> 原文链接：https://robotics.hellobike.com/papers/2026-10-07-2610-09891
> 频道：papers | 更新日期：2026-10-07 | 标签：arxiv, cs.RO
> 核心摘要：针对HRC中RLHF的安全与反馈挑战，论文综述相关方法，并以VR实验验证反馈发起方式对安全感知的影响。

# 面向人机协作的基于人类反馈的强化学习的范围综述与实验研究

::: v-pre
> 原文链接：[A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration](https://arxiv.org/abs/2610.09891)（arXiv 2610.09891 · 发布日期：2026-10-07）

**A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration**

针对HRC中RLHF的安全与反馈挑战，论文综述相关方法，并以VR实验验证反馈发起方式对安全感知的影响。

## 核心要点

- HRC有助于工业4.0大规模定制，但RLHF仍面临开发安全、反馈质量和双向适应挑战。
- 综述按PRISMA筛选199条记录，纳入2020—2025年20篇同行评审论文并映射方法。
- 实验在VR中比较系统发起和用户发起反馈，用贝叶斯模型分析反馈与心理、物理安全指标关系。
- 结果显示用户发起反馈更能捕捉感知安全，未来应重视更真实HRC实验和反馈方法评估。

**验证范围与局限**：综述范围为2020—2025年20篇同行评审论文；实证验证限于VR中的机器人近体行为安全导航。

## 英文摘要 (Abstract)

Human-Robot Collaboration (HRC) can facilitate mass customisation in Industry 4.0, with Reinforcement Learning from Human Feedback (RLHF) representing a promising approach for developing safe AI-based robots. Practical challenges remain regarding safety during AI development, human feedback quality, and bidirectional human-robot adaptation. We conducted a scoping review of RLHF in HRC systems, mapping methods that address these challenges. Following PRISMA guidelines, we screened 199 records and included 20 peer-reviewed publications (2020-2025) spanning multiple HRC domains. To our knowledge, this is the first review focused on the bidirectional, closed-loop design of RLHF. Our review found multiple feedback modalities enabling data collection in various feedback formats. Collected data can be integrated at different stages of AI training, resulting in a multi-step development process. Pilot experiments are commonly used to evaluate HRC systems based on both human and robot metrics. To empirically test a key gap identified in the review, we conducted a between-subjects VR experiment comparing system- and user-initiated feedback on robot proxemic behaviour for safe navigation. Using Bayesian models, we analysed the relation between the collected feedback and safety metrics: psychological safety (post-experiment questionnaire) and physical safety (inverse time-to-collision). Results show that user-initiated feedback captures perceived safety better than system-initiated feedback, indicating that feedback timing directly affects feedback quality. Our review and experiment findings show that RLHF relies on appropriate feedback methods to ensure AI safety in HRC, and future RLHF research should prioritise realistic HRC experiments evaluating the effects of feedback collection methods on relevant human and robot metrics.

作者：Alexandra Coroiu, Andrea Vogt, Viktor Werbilo, Andreas Poppele, Johann Christensen, Sven Hallerbach
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## 原始参考来源 (Sources)

- [arXiv 2610.09891：A Scoping Review and Experimental Study on Reinforcement Learning from Human Feedback for Human-Robot Collaboration](https://arxiv.org/abs/2610.09891)