https://www.oaepublish.com/articles/ir.2026.05

Abstract 摘要

Human–robot collaboration (HRC) has traditionally relied on instruction-driven paradigms in which humans specify goals and robots execute predefined tasks. Recent advances in embodied artificial intelligence (Embodied AI) challenge this model by grounding intelligence in physical embodiment and continuous interaction with the environment. This editorial positions Embodied AI as a paradigm shift in HRC, redefining collaboration as a physically interactive and mutually adaptive process. It examines the key challenges introduced by this shift and outlines emerging directions for future embodied HRC.

人机协作（HRC）传统上依赖于指令驱动的范式，即人类指定目标，机器人执行预设任务。近期具身人工智能（Embodied AI）的进展通过将智能扎根于物理身体和与环境的持续互动，挑战了这一模型。本论文将具身人工智能定位为 HRC 领域的范式转变，重新定义协作为一种物理互动且相互适应的过程。报告探讨了这一转变带来的关键挑战，并概述了未来具象化 HRC 的新兴方向。

Keywords 关键词
Embodied artificial intelligence, Human–robot collaboration, human-centered interaction, trustworthy collaboration
具身人工智能、人机协作、以人为本的互动、可信赖的协作

1. INTRODUCTION
1. 引言
Human–robot collaboration (HRC) has long been envisioned as a future development of intelligent automation, with applications spanning manufacturing, healthcare, service robotics, and assistive technologies. During recent decades, remarkable progress has been achieved in motion planning, safety-aware control, intention recognition, and task allocation, enabling robots to collaborate with humans. However, most existing HRC systems remain fundamentally instruction-driven, where humans specify goals or constraints, and robots execute tasks within predefined operational envelopes.

人机协作（HRC）长期以来被视为智能自动化的未来发展，应用领域涵盖制造、医疗、服务机器人和辅助技术。近几十年来，在运动规划、安全感知控制、意图识别和任务分配方面取得了显著进展，使机器人能够与人类协作。然而，大多数现有 HRC 系统仍然基本上是指令驱动的，由人类指定目标或约束，机器人在预设的操作范围内执行任务。

Recently, embodied artificial intelligence (Embodied AI) has emerged as a growing research trend to emphasize the inseparable coupling between perception, action, and physical embodiment. Instead of treating artificial intelligence (AI) as a purely computational process separate from the physical world, Embodied AI posits that cognition arises through continuous interaction between the physical agent and the surrounding environment.

近年来，具身人工智能（Embodied AI）成为一个日益增长的研究趋势，强调感知、行动与身体身体之间不可分割的联系。具身人工智能不将人工智能（AI）视为与物理世界分离的纯粹计算过程，而是认为认知通过物理主体与周围环境之间的持续互动产生。

This editorial argues that Embodied AI is not merely an enabling technology for HRC, but a paradigm shift that redefines the nature of collaboration itself. Specifically, Embodied AI redefines HRC as a physically interactive process of mutual interaction in real-time, rather than a sequence of predefined task executions. This shift has profound implications for how robots are designed and how long-term human–robot partnerships are conceptualized.

本文认为，具身人工智能不仅是人力资源管理的辅助技术，更是重新定义协作本质的范式转变。具体来说，具身人工智能将 HRC 重新定义为一种实时物理交互的交互过程，而非一系列预设任务执行。这一转变对机器人的设计方式以及长期人机合作的构想具有深远影响。

2. CHALLENGES
2. 挑战
As with any new technological paradigm, the application of Embodied AI to HRC entails both opportunities and challenges.

与任何新技术范式一样，具身人工智能在人力资源管理中的应用既有机遇，也伴随着挑战。

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2.1. Modeling and learning mutual collaborative embodiments
2.1. 建模与学习相互协作的体现
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A fundamental challenge lies in how to formally model and learn mutual embodiment between humans and robots. Most existing HRC systems remain robot-centric, with human behavior modeled as context rather than as a co-evolving component of the system. Mutual embodiment requires adaptiveness to human-to-robot and robot-to-human interactions across individual differences and contextual variability. From this perspective, challenges such as human intent inference, role negotiation, timing and turn-taking, and the development of team fluency are not separate issues but core requirements for realizing mutual collaborative embodiment. The difficulty of learning the co-adaptive representations is further exacerbated by the scarcity of high-quality human–robot interaction data and the persistent sim-to-real gap, where contact dynamics, human variability, and task diversity limit generalization across platforms and environments.

一个根本性的挑战在于如何正式建模和学习人类与机器人之间的相互体现。大多数现有的 HRC 系统仍以机器人为中心，人类行为被建模为情境，而非系统中共同进化的组成部分。相互具象需要适应人与机器人、机器人与人之间的互动，跨越个体差异和情境变异。从这个角度看，人类意图推断、角色协商、时机与轮流、团队流利度的发展等挑战并非独立问题，而是实现相互协作体现的核心要求。学习共适应表示的难度因高质量人机交互数据稀缺以及持续存在的模拟与现实差距而加剧，接触动态、人类变异性和任务多样性限制了跨平台和环境的泛化。

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2.2. Integrating physical and cognitive human states
2.2. 整合物理与认知人类状态
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Although many studies have been conducted on the detection of human fatigue, workload, and affective states, these factors are still predominantly treated as external constraints or safety triggers. Embodied HRC demands a shift toward the deep integration of human physical and cognitive states into the control and learning loop, where such states actively shape robot behavior in real time. Beyond low-level physical states, effective embodied collaboration also requires continuous inference of higher-level cognitive states, including human intent, preferences, acceptable risk, and evolving task understanding. A major challenge is how to reconcile the high-level cognition with the stringent real-time control requirements of embodied interaction, where tight feedback loops involving force, compliance, and micro-adjustments are essential for fluent and safe collaboration.

尽管已有许多关于人体疲劳、工作量和情感状态检测的研究，但这些因素仍主要被视为外部约束或安全触发因素。具身的 HRC 要求将人类的身体和认知状态深度整合进控制和学习循环，这些状态实时主动塑造机器人行为。除了低层次的物理状态外，有效的具身协作还需要持续推断更高层次的认知状态，包括人类意图、偏好、可接受风险以及不断演变的任务理解。一个重大挑战是如何调和高层认知与具身互动中严格的实时控制要求，在互动中，涉及强制、顺从和微调的紧密反馈回路对于流畅且安全的协作至关重要。

2.3. Safety and responsibility in embodied collaboration 2.3. 安全与责任在具象协作中体现 As AI becomes physically embodied, its decisions and actions increasingly have direct impacts on human well-being, and in some cases on safety and life itself, particularly in domains such as healthcare and autonomous driving. The physical embodiment of AI transforms decision-making from abstract computation into real-world action, raising fundamental questions about moral responsibility, accountability, and ethical governance. At the technical level, embodied HRC should ensure safety in physical interaction, where errors can lead to collisions, tool misuse, unsafe trajectories, or harmful forms of over-assistance in long-horizon tasks.

随着人工智能逐渐具象化，其决策和行为对人类福祉产生越来越直接的影响，在某些情况下甚至影响安全和生命本身，尤其是在医疗和自动驾驶等领域。人工智能的物理化将决策从抽象计算转变为现实世界的行动，提出了关于道德责任、问责制和伦理治理的根本性问题。在技术层面，具身 HRC 应确保物理互动的安全，错误可能导致碰撞、工具误用、不安全轨迹或长期任务中的有害过度协助。

3. PERSPECTIVES
3. 观点
Embodied AI opens up new perspectives and possibilities for the evolution of HRC to encourage a shift toward more adaptive and human-centered forms of collaboration.

具身人工智能为 HRC 的发展开辟了新的视角和可能性，鼓励更适应性和以人为本的协作形式转变。

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3.1. Humanoid embodied systems
3.1. 类人生物具身系统
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Humanoid embodied systems represent a compelling pathway toward more transferable and scalable forms of HRC. By sharing similar body structures, sensorimotor capabilities, and action spaces with humans, humanoid robots offer a promising basis for learning from human demonstrations. This structural alignment may help narrow the gap between human intent and robotic execution, supporting collaborative behaviors that extend across tasks, environments, and application contexts. Future platforms should be well-suited to emerging architectures that combine embodied foundation models for semantic understanding and task decomposition with modular planning, verification, and low-level control stacks for contact-rich execution. In practice, embodied HRC could begin in structured and constrained environments, where the reliability and cost barriers of humanoid systems can be addressed before broader deployment in open-world settings.

人形具身系统代表了迈向更具可转移性和可扩展性 HRC 形式的有力路径。通过与人类共享相似的身体结构、感觉运动能力和动作空间，类人机器人为从人类演示中学习提供了有前景的基础。这种结构性一致性有助于缩小人类意图与机器人执行之间的差距，支持跨任务、环境和应用环境的协作行为。未来的平台应适合新兴架构，这些架构结合了具身基础模型用于语义理解和任务分解，同时还包括模块化规划、验证和低层控制栈，实现丰富的接触执行。实际上，具身 HRC 可以从结构化且受限的环境中开始，在那里可以解决类人生物系统的可靠性和成本障碍，然后再在开放世界中更广泛部署。

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3.2. Extension of human cognition and affect
3.2. 人类认知与情感的延伸
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Beyond task execution and adaptive control, Embodied AI invites a reconceptualization of artificial agents as extensions of human cognition and affect. By embedding intelligence within a physical body that continuously interacts with humans, Embodied AI systems can ground perception, decision-making, and interaction in meaningful social and emotional contexts. This enables robots to perceive and respond to affective signals such as engagement, stress, and trust. The future study could broaden the scope of HRC by positioning perception and sociality as embodied phenomena, while raising new questions regarding ethical responsibility and the societal role of emotionally responsive artificial agents. From this perspective, the shift from pre-programmed collaboration toward co-adaptive interaction reflects a move to shared autonomy, in which robots ask clarifying questions, negotiate roles, and learn user-specific conventions while keeping humans in meaningful control.

超越任务执行和自适应控制，具身人工智能还邀请人们重新构想人工代理作为人类认知和情感的延伸。通过将智能嵌入持续与人类互动的实体身体中，具身的人工智能系统能够将感知、决策和互动扎根于有意义的社会和情感环境中。这使得机器人能够感知并响应情感信号，如投入、压力和信任。未来的研究可能通过将感知和社会性定位为具身现象，拓宽 HRC 的范围，同时提出关于伦理责任及情感响应人工代理社会角色的新问题。从这个角度看，从预设协作向共适应交互的转变反映了向共享自主的转变，机器人提出澄清性问题、协商角色并学习用户专属的惯例，同时保持人类的有意义控制权。

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3.3. Trustworthy human–robot partnerships
3.3. 值得信赖的人机合作
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Embodied AI might enable a transition from short-term task assistance to sustained human–robot partnerships. By grounding robot behavior in shared embodiment and continuous adaptation, collaboration can become more intuitive, resilient, and trust-aware. Human-robot partnerships will be particularly valuable in domains where human conditions, environments, and objectives evolve over time. Although the vision of trustworthy human–robot partnerships emphasizes long-term collaboration, real-world adoption is likely to progress unevenly, with earlier success in structured and constrained domains before broader deployment in open-world settings. This progression underscores the importance of reliability, transparency, and recoverability as foundational properties for trust-aware deployment rather than secondary design considerations.

具身人工智能可能使短期任务协助向持久的人机合作转变。通过将机器人行为扎根于共享的身体和持续适应，协作可以变得更加直观、韧性和信任意识。人机合作在人类条件、环境和目标随时间演变的领域尤为宝贵。尽管可信的人机合作伙伴关系愿景强调长期合作，但现实世界的采用进展可能不均衡，先在结构化且受限的领域取得早期成功，随后才会在开放世界环境中更广泛部署。这一进展强调了可靠性、透明度和可恢复性作为信任感知部署的基础属性，而非次级设计考量的重要性。

4. CONCLUSION
4. 结论
Embodied AI marks a fundamental shift in how HRC is conceptualized, moving beyond instruction-driven task execution toward physically grounded and mutually adaptive interaction. This editorial has highlighted that while the embodied HRC introduces substantial challenges in modeling, learning, safety, and responsibility, it also opens promising perspectives for humanoid systems, shared cognition, and long-term human–robot partnerships. Importantly, the significance of embodied HRC is to enable collaboration that is adaptive, trustworthy, and centered on human needs and values. Realizing this vision will require rethinking system architectures, learning paradigms, and evaluation criteria across both research and deployment. Finally, Embodied AI invites the robotics community to reconsider not only how robots act, but how humans and robots coexist and collaborate in the physical world.

具身人工智能标志着 HRC 概念的根本转变，从指令驱动的任务执行转向物理化且互适应的互动。本文强调，虽然具身的 HRC 在建模、学习、安全和责任方面带来了重大挑战，但它也为类人系统、共享认知以及长期人机伙伴关系开辟了有前景的前景。重要的是，具身 HRC 的重要性在于促进适应性强、可信赖且以人类需求和价值观为中心的协作。实现这一愿景需要重新思考系统架构、学习范式和评估标准，涵盖研究和部署。最后，具身人工智能邀请机器人界重新思考不仅是机器人的行为方式，还要重新思考人类与机器人在物理世界中的共存与协作。