Shared Control/Autonomy: A Historical Perspective, Current Trends, and the Role of Generative AI

Michael Hagenow1 , Mario Selvaggio2 , Xuehui Yu3 , Yanwei Wang4 , Yiannis Demiris5 , Andreea Bobu6 , Yilun Du7 , Harold Soh3 , Dylan Losey8 , Julie Shah6
1 University of Wisconsin - Madison · 2 University of Naples Federico II · 3 National University of Singapore · 4 Generalist AI · 5 Imperial College London · 6 Massachusetts Institute of Technology · 7 Harvard University · 8 Virginia Tech
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Overview

In shared control and shared autonomy systems, humans collaborate with robot agents to achieve common goals. Research in this area dates back over 40 years, with numerous applications, such as in manufacturing, robot surgery, and assistive technologies. Shared control approaches have even seen some commercialization efforts in areas like semi-autonomous driving and automotive assembly. Recently, shared control and shared autonomy approaches have gained significant traction, with hundreds of new methods published in scientific papers each year. In this paper, we examine recent approaches and trends in these methods, investigating several crucial aspects that are underexplored in previous surveys. First, we provide descriptive statistics and trends related to human input methods, technical approaches, and applications. Second, we examine the growing role of generative artificial intelligence approaches in shared control and autonomy. Based on these insights, we offer updated recommendations for future approaches.

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Citation

@article{hagenow2025shared,
  title={Shared Control/Autonomy: A Historical Perspective, Current Trends, and the Role of Generative AI},
  author={Hagenow, Michael and Selvaggio, Mario and Yu, Xuehui and Wang, Yanwei and Demiris, Yiannis and Bobu, Andreea and Du, Yilun and Soh, Harold and Losey, Dylan and Shah, Julie},
  journal={Authorea Preprints},
  year={2025},
  publisher={Authorea},
  doi={10.36227/techrxiv.176617724.41163595/v1}
}