In the field of human-robot interaction, robot autonomy and intuitiveness are usually conflictual design requirements. The amount of physical and cognitive effort requested to the user in executing a task is determined by the robot autonomy level. To enable the operator to physically interact with robots in an intuitive and unobtrusive way, the robot may need to adapt its autonomy level seamlessly while performing a complex task.
Nowadays, research on shared autonomy systems is playing a relevant role in a wide spectrum of robotic applications ranging from surgical to industrial robotics. Recently, the availability of datasets and the advancement of machine learning techniques have enabled enhanced flexibility of shared autonomy systems that are now capable of providing contextual or personalized assistance and seamless adaption of the autonomy level. However, this desirable trend raises new challenges for safety and stability certification of shared autonomy robotic systems, thus requiring new advanced control methods to implement the continuously evolving division of roles.
The aim of this special issue is to collect the latest results on shared autonomy and its applications to physical human-robot interaction. Particular interest is devoted to approaches that combine learning and control into unified frameworks in different domains.
Topics of interest for this special issue include and are not limited to:
- Shared autonomy and supervisory control architectures
- Human-robot (physical) interaction and collaboration
- Modeling, learning and control human-robot interaction
- Collaborative and assistive robotics
- Telerobotics control and haptic feedback interfaces
- Co-adaptation between human and robot
- Intention recognition, skill level/gap evaluation and role allocation
- Learning from demonstration
- Human-aware motion planning
- Applications of shared autonomy in hazardous, surgical environments
Submission deadline: 24 February 2021
Publication date: 23 August 2021
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