Human-Centered Shared Autonomy for Motor Planning, Learning, and Control Applications

Abstract: With recent advancements in AI and computational tools, intelligent paradigms have emerged to enhance fields like shared autonomy and human-machine teaming in healthcare. Advanced AI algorithms (e.g., reinforcement learning) can autonomously make decisions to achieve planning and motion goals. However, in healthcare, where human intent is crucial, fully independent machine decisions may not be ideal. This chapter presents a comprehensive review of human-centered shared autonomy AI frameworks, focusing on upper limb biosignal-based machine interfaces and associated motor control systems, including computer cursors, robotic arms, and planar platforms. We examine motor planning, learning (rehabilitation), and control, covering conceptual foundations of human-machine teaming in reach-and-grasp tasks and analyzing both theoretical and practical implementations. Each section explores how human and machine inputs can be blended for shared autonomy in healthcare applications. Topics include human factors, biosignal processing for intent detection, shared autonomy in brain-computer interfaces (BCI), rehabilitation, assistive robotics, and LLMs as the next frontier. We propose adaptive shared autonomy AI as a high-performance paradigm for collaborative human-AI systems, identify key implementation challenges, and outline future directions, particularly regarding AI reasoning agents. This analysis aims to bridge neuroscientific insights with robotics to create more intuitive, effective, and ethical human-machine teaming frameworks.