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

TL;DR

This paper reviews human-centered shared autonomy AI frameworks in healthcare, emphasizing biosignal-based interfaces and motor control, and proposes adaptive AI paradigms for improved human-machine collaboration.

Contribution

It provides a comprehensive analysis of current shared autonomy systems in healthcare, integrating neuroscientific insights with robotics, and introduces adaptive AI approaches for enhanced collaboration.

Findings

Analysis of biosignal processing for intent detection

Discussion of shared autonomy in brain-computer interfaces

Identification of challenges and future directions in AI-human teaming

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 Large Language Models (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.