Editorial: Human-in-the-loop paradigm for assistive robotics

摘要

The collaboration between humans and robots has become a central theme in the evolution of assistive technologies. In domains such as rehabilitation, prosthetics, and daily living assistance, the paradigm of human-in-the-loop introduces a new perspective: rather than designing robots to act independently, robotic systems are endowed with mechanisms to incorporate human inputs, feedback, and intentions directly into their control and learning processes. This integration facilitates adaptation, personalization, and mutual trust between humans and robotic agents, thereby improving usability and acceptance in real-world scenarios.A crucial element of this paradigm is the ability to continuously model the human state, encompassing physical, cognitive, and affective dimensions, to capture user intent, fatigue, emotion, and attention in real time. Such online modeling is essential for achieving robots that can dynamically adapt their behaviour to changing human conditions and needs and ensure intuitive, empathetic interaction.While autonomous robots excel in precision, repeatability, and trajectory execution, humans bring situational awareness, contextual decision-making, and corrective actions that are still difficult for machines to achieve. The human-in-the-loop paradigm combines the strengths of humans and robots to develop systems that are technically effective and designed with the human user at their core. Within assistive robotics, this is particularly critical: the interaction must account for unpredictability, support the user's individual needs, and guarantee safety, transparency, and trustworthiness.Despite the significant progress in robotics, outcomes in rehabilitation and assistive domains are still limited. Clinical results from robot-aided therapies often remain comparable to traditional interventions, and prosthetic devices still fall short of replicating the natural behaviour of biological systems. These challenges highlight the need for research on how human feedback, intention recognition, multimodal interaction, and personalized control can be used to improve assistive robotic systems. This Research Topic on the "Human-in-the-loop paradigm for assistive robotics" brings together contributions that highlight novel methods, experimental studies, and conceptual frameworks addressing these open issues. The collection includes approaches on hapticaugmented teleoperation, explainable trajectory corrections, movement recognition and myoelectric control for muscular dystrophy patients. Together, these works shed light on how human-in-the-loop strategies can improve adaptability, intuitiveness, and effectiveness in assistive robotics.Across these studies, a common thread is the need to sense and model the user state in real time, enabling robots to adapt continuously and maintain effective and safe interaction. Such capability enables human-robot co-adaptation and paves the way for next-generation assistive systems that can respond not only to physical cues but also to cognitive and emotional feedback.One promising direction in human-in-the-loop robotics lies in enhancing transparency and sensory augmentation during interaction. The study by van den Berg et al. [1] addresses this by integrating force visualization into VR teleoperation environments. By augmenting visual feedback with representations of interaction forces, the system enables operators to better understand and control their actions, particularly in haptic-assisted scenarios. This approach demonstrates how multimodal feedback can increase task performance and user confidence, highlighting the role of perceptual augmentation in the human-in-the-loop framework.A related challenge is how humans communicate corrections and preferences to robotic systems. Yow et al. [2] introduce a framework where users can provide trajectory corrections through natural language. In particular, the system produces explainable corrections, linking adjustments to textual descripti