让机器人更懂人,让人更会指挥机器人,实现无缝协作。
Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human-Robot Collaboration
- 用多模态输入理解人类指令与意图
- 动态调整任务分配与角色分工
- 适合希望提升人机协作效率的研究者
机器人与人工智能在复杂任务和环境中已取得显著进展,但人类常处于被动观察状态,难以有效参与。机器人在有人环境中也无法充分发挥潜力,因缺乏对人类状态与意图的有效建模及行为自适应能力。为实现高效的人-机协同(HRC),需建立持续的信息流:人类应能直观传达指令、分享专长并表达需求;机器人则需清晰传递内部状态与下一步动作,以维持用户知情、舒适与控制感。本文综述了促进人机间直观信息交换与技能转移的关键组件,涵盖从人类到机器的多模态输入转换,经自适应规划与角色分配,至控制层与反馈机制的完整交互流程。最后,展望了更具适应性与可及性的未来方向。
原文摘要 · Abstract (English)
Remarkable capabilities have been achieved by robotics and AI, mastering complex tasks and environments. Yet, humans often remain passive observers, fascinated but uncertain how to engage. Robots, in turn, cannot reach their full potential in human-populated environments without effectively modeling human states and intentions and adapting their behavior. To achieve a synergistic human-robot collaboration (HRC), a continuous information flow should be established: humans must intuitively communicate instructions, share expertise, and express needs. In parallel, robots must clearly convey their internal state and forthcoming actions to keep users informed, comfortable, and in control. This review identifies and connects key components enabling intuitive information exchange and skill transfer between humans and robots. We examine the full interaction pipeline: from the human-to-robot communication bridge translating multimodal inputs into robot-understandable representations, through adaptive planning and role allocation, to the control layer and feedback mechanisms to close the loop. Finally, we highlight trends and promising directions toward more adaptive, accessible HRC.
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