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Creators/Authors contains: "Huang, Chien-Ming"

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  1. ; ; ; ; ; ; (, 2025 Robotics: Science and Systems Conference)
    Free, publicly-accessible full text available June 23, 2026
  2. ; ; ; ; ; ; (, 2025 Robotics: Science and Systems Conference)
    Free, publicly-accessible full text available June 23, 2026
  3. ; ; (, ACM)
    Free, publicly-accessible full text available March 4, 2026
  4. ; ; (, IEEE)
    Free, publicly-accessible full text available March 4, 2026
  5. ; ; ; ; (, International Journal of Human-Computer Studies)
    Free, publicly-accessible full text available January 1, 2026
  6. ; ; ; ; ; ; (, Radiology)
    In this multisite prospective study of simulated artificial intelligence (AI)–assisted chest radiograph diagnosis involving 220 physicians, AI explanation type (local vs global) differentially impacted physician diagnostic performance and trust in AI advice. 
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    Free, publicly-accessible full text available November 1, 2025
  7. ; ; ; (, ACM)
    Full Text Available 
  8. ; (, Proceedings of the AAAI Symposium Series)
    Large language models offer new ways of empowering people to program robot applications-namely, code generation via prompting. However, the code generated by LLMs is susceptible to errors. This work reports a preliminary exploration that empirically characterizes common errors produced by LLMs in robot programming. We categorize these errors into two phases: interpretation and execution. In this work, we focus on errors in execution and observe that they are caused by LLMs being “forgetful” of key information provided in user prompts. Based on this observation, we propose prompt engineering tactics designed to reduce errors in execution. We then demonstrate the effectiveness of these tactics with three language models: ChatGPT, Bard, and LLaMA-2. Finally, we discuss lessons learned from using LLMs in robot programming and call for the benchmarking of LLM-powered end-user development of robot applications. 
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    Full Text Available 
  9. ; ; ; (, ACM)
    Full Text Available 
  10. ; ; (, International Journal of Human-Computer Studies)
    Full Text Available