Human-robot interaction (HRI), collaborative robotics (CR), and assistive robotics (AR) are promising research areas to improve the quality of human life by helping individuals in occupational tasks, routine activities, and therapy programs. At the same time, robotic systems have benefited from the advancement of Artificial Intelligence (AI) to perceive their environment and achieve their goals autonomously. However, the safety of human operators/users is still a concern while increasing the autonomy of robotic systems in HRI applications, which necessitates conservative stability considerations and boundedness of the robot response. In this workshop, innovative ideas from the experts and outstanding researchers actively contributed to the field of HRI, CR, and AR will be introduced to broaden the vision on the development of AI-based techniques, Machine Learning (ML) algorithms, and intelligent control schemes that can facilitate a balance between robot autonomy and human safety in various applications. The workshop is organized to include interactive keynote talks as well as round-table discussions and poster presentations. This workshop will provide an excellent venue for researchers from academia and industrial sectors to engage in discussions of future innovations for autonomous HRI, and intelligent CR and AR.

In HRI, CR, and AR systems, humans are the recipient of physical assistance provided by the robot or they perform a collaborative task together with the robot. To enhance the human’s authority and safety, advanced control schemes have been developed to adapt the robot's actions to the human’s behavior by monitoring the interaction between the human and the robot and ensuring the robot’s stability during the interaction. The current trend in the field is to enhance the autonomy of robotic systems to make the interactions between humans and robots more reliable and comfortable through more effective collaborations. To this end, the field can benefit more from the integration of AI into robot controllers while preserving safe and stable behavior in interaction with the human operator. The balance between autonomy and safety is technically more challenging considering the non-passive and unpredictable behaviors of humans that make the detection of their intention harder. Accordingly, recent research studies have been dedicated to investigating intelligent strategies for the estimation, control, and learning of HRI using model-based and model-free techniques. Dynamic modeling of the human and robot in different tasks has been effectively employed in some applications to predict their behavior and design appropriate control methodologies to ensure stability conditions. On the other hand, due to the complexity of these models and the requirement of online calibrations, recent studies have focused on the employment of model-free strategies composed of ML algorithms or a combination of them with adjustable and adaptive controllers.

This workshop will gather viewpoints from leading researchers who have been extensively investigating the development of autonomous HRI and intelligent CR and AR in different applications. Various robotic systems including manipulators, upper-limb and lower-limb exoskeletons, mobile robots, humanoid and bioinspired robots will be discussed with particular focus on their physical and social interactions with human environments. These interactions can be in industrial applications such as manufacturing, warehousing, and construction that require manual materials handling to prevent occupational injuries, and also in clinical and home-based assistance of individuals with physical impairments and neurological conditions in therapeutic and routine activities. Special emphasis will be on the integration of computational AI-based techniques and analytical methodologies at both high-level decision making and low-level motion, force, and/or impedance control in HRI, CR, and AR. This workshop will recognize how we can elevate the intelligence and autonomy of robots and exoskeletons while maintaining the authority and safety of humans from different control aspects including perception, planning, and decision-making. In this regard, balancing the level of robot autonomy and human safety have excellent yet untapped potential for meeting the needs of more efficient and comfortable HRI in various collaborative and assistive tasks.

This workshop will initiate constructive discussions to come up with effective roadmaps and policies to enhance the shared autonomy between robots and humans for further empowerment of HRI using AI and ML in the future. Topics of interest are mentioned below, which are applicable to manipulators, exoskeletons, mobile, humanoid and bioinspired robots in different applications of HRI, CR, and AR:

• Shared autonomy between human and robot

• Impedance/force variation and learning

• Autonomous motion planning

• Intelligent adaptive controllers

• Robot perception and human intention detection

• High-level and low-level decision making

• Interaction modeling and estimation

• Machine Learning (ML) and Artificial Intelligence (AI)

• Computational and analytical methods for robot control and learning