Abstract
This is a preprint of a review article that has not yet undergone peer review. The content is intended for early dissemination and academic discussion. The final version may differ upon formal publication. As the Fourth Industrial Revolution reshapes industrial paradigms, human-robot collaboration (HRC) has transitioned from a desirable capability to an operational necessity. In response, collaborative robots (Cobots) are evolving beyond repetitive tasks toward adaptive, semantically informed interaction with humans and environments. This paper surveys five foundational pillars enabling this transformation: semantic-level perception, cognitive action planning, explainable learning and control, safety-aware motion design, and multimodal human intention recognition. We examine the role of semantic mapping in transforming spatial data into meaningful context, and explore cognitive planning frameworks that leverage this context for goal-driven decision-making. Additionally, we analyze explainable reinforcement learning methods, including policy distillation and attention mechanisms, which enhance interpretability and trust. Safety is addressed through force-adaptive control and risk-aware trajectory planning, while seamless human interaction is supported via gaze and gesture-based intent recognition. Despite these advancements, challenges such as perception-action disjunction, real-time explainability limitations, and incomplete human trust persist. To address these, we propose a unified Cognitive Synergy Architecture, integrating all modules into a cohesive framework for truly human-centric cobot collaboration.
Abstract (translated)
这是一篇尚未经过同行评审的综述文章的预印本。内容旨在早期传播和学术讨论，正式发表时可能会有所更改。随着第四次工业革命重塑产业范式，人机协作（HRC）已经从一种可取的能力转变为运营上的必要性。为此，协作机器人（Cobots）正在超越重复性的任务，迈向适应性强、语义驱动的人机及环境互动。本文概述了实现这一转型的五大基础支柱：语义级感知、认知行为规划、解释性学习和控制、安全意识的动作设计以及多模态人类意图识别。 我们探讨了语义映射在将空间数据转化为有意义上下文中的作用，并研究了利用这种背景信息进行目标导向决策的认知计划框架。此外，本文还分析了解释性强化学习方法（包括策略蒸馏和注意力机制），这些方法增强了解释性和信任度。安全问题通过力适应控制及风险感知轨迹规划得到解决；而无缝的人机互动则由注视与手势意图识别来支持。 尽管取得了上述进展，但诸如感知-行动断层、实时可解释性限制以及人类信任不足等问题仍然存在挑战。为应对这些挑战，我们提出了一种统一的认知协同架构，将所有模块整合进一个连贯的整体框架中，从而实现真正以人为本的协作机器人合作。
URL
https://arxiv.org/abs/2505.03815
