With the progress and development of human-robot systems, the coordination among humans, robots, and environments has become increasingly sophisticated. In this Research Topic, we focus on an important field in robotics and automation disciplines, which is commonly defined as behavior-assistant robots. The scope includes but is not limited to: (1) rehabilitation assistive devices, such as rigid/soft exoskeletons, prosthetic systems, orthoses, and intelligent wheelchairs; (2) intelligent medical systems, such as endoscopic robots, surgical robots, and the navigation systems; (3) industrial application devices, such as collaborative manipulators, load-bearing exoskeletons, supernumerary robotic limbs; (4) intelligent domestic devices, such as mobile robots, elderly-care robots, walking-aids robots and so on. The emergence of robot-assisted daily behaviors, based on aforementioned devices, is gradually becoming part of our social lives, which can improve weak motor abilities, enhance physical functionalities, and enable various other benefits.

For the effective operation of a behavior-assistant robot, one of the most commonly used control strategies is the human-in-the-loop (HIL) control architecture. As widely recognized, the analysis of technological pathways for behavior-assistant robots could be approached from two perspectives: one is “robot and automation”, where we focus on mechanism and design, actuation, control, hardware and integration; of the other is “human and environment”, where we focus on sensors, data, information fusion, pattern recognition, decision and interaction. Further connection of these core technologies forms HIL systems, and has achieved significant progress so far.

However, the majority of HIL-based robots have not yet achieved stable closed-loop system design, and the integration of human-robot interaction remains a challenge. For example: in recent years, rigid/soft exoskeletons have gained significant attention. HIL control using physiological and image information has been realized in Hybrid Assistive Limb exoskeleton (HAL) and the Exosuit system (Wyss Institute, Harvard University). However, there is still a considerable gap to be bridged before advanced technologies can be widely applied in clinical rehabilitation settings, especially for improving the safety of HIL systems and neural interaction control. For surgical robots and manipulator arms, they have exhibited high precision in repetitive positioning and accurate force control. However, there is ongoing research on how to create a more comfortable and user-friendly environment for surgeons to perform robot-assisted surgeries. Meanwhile, the application scenarios for domestic robots are more diverse. It is crucial to explore how to make devices more user-friendly for the elderly, patients, and people with disabilities. Further research is needed to enhance the practicality of automation devices in these contexts. In summary, how to design stable HIL systems for behavior-assistant robots still requires further investigation.

In addition, there is little research work at this stage to truly realize the intelligence of HIL systems from theoretical, technical, and application perspectives. One important reason is the adaptation issue. Indeed, the most common characteristics of behavior-assistant robots are that the participants/users are diverse, and the devices are generally designed to be adaptable to different individuals. Thus, how to ensure stable adaptation for different individuals is a highly challenging problem. Additionally, human-robot systems also face challenges from environmental factors. For instance, rehabilitation devices such as prostheses, exoskeletons and orthoses, often encounter various walking environments (such as stairs, slopes, etc.). Collaborative manipulators such as surgical robots and similar devices, often face uncertainties in terms of objects and control tasks. In summary, how to realize an adaptive HIL robotic system and to robustly cope with uncertainties, is a newly emerging research area and could generate breakthrough results.

We cordially welcome authors to submit their primary/original research papers and reviews to contribute to this Research Topic. Research Report, Mini Reviews, Opinion, and other types are also fine. Topic areas include but are not limited to:
• Sensors development, multimodal information fusion, and pattern recognition for mechanical/ physiological electrical/image information;
• Innovative structural design, hardware development, system integration, interaction control, and verification of behavior-assistant robot;
• Human-robot interaction control for behavior-assistant robots;
• Human-robot-environment fusion for behavior-assistant robots;
• Adaptive methods and technologies in HIL systems;
• Practical applications of HIL robotic control systems.

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