Intuitive Programming, Adaptive Task Planning, and Dynamic Role Allocation in Human–Robot Collaboration

Abstract

Robotics and AI have achieved remarkable capabilities, including 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, a continuous information flow should be established: Humans must intuitively communicate instructions, share expertise, and express needs, while 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 that translates 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 human–robot collaboration.