@inproceedings{garcia-anakabe-etal-2026-adding,
    title = "Adding Determinism to a Dialogue Agent for a Robotic Environment",
    author = "Garcia Anakabe, Oihana  and
      Cocola, Riccardo  and
      Aceta, Cristina",
    editor = "Riccardi, Giuseppe  and
      Mousavi, Seyed Mahed  and
      Torres, Maria Ines  and
      Yoshino, Koichiro  and
      Callejas, Zoraida  and
      Chowdhury, Shammur Absar  and
      Chen, Yun-Nung  and
      Bechet, Frederic  and
      Gustafson, Joakim  and
      Damnati, G{\'e}raldine  and
      Papangelis, Alex  and
      D{'}Haro, Luis Fernando  and
      Mendon{\c{c}}a, John  and
      Bernardi, Raffaella  and
      Hakkani-Tur, Dilek  and
      Di Fabbrizio, Giuseppe {''}Pino{''}  and
      Kawahara, Tatsuya  and
      Alam, Firoj  and
      Tur, Gokhan  and
      Johnston, Michael",
    booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
    month = feb,
    year = "2026",
    address = "Trento, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard-stats/2026.iwsds-1.26/",
    pages = "253--261",
    abstract = "Large Language Models ({LLM}s) have strong capabilities in natural dialogue, but their inherent indeterminacy presents challenges in robotic environments where safety and reliability are critical. In this study, we propose a dialogue agent that has been developed to guide and support human operators during robot demonstrations, following the Learning from Demonstration ({L}f{D}) paradigm, where the robot learns tasks from the operator{'}s actions. The agent presented in this work extends the standard prompt-based {LLM} setup by integrating state graphs that explicitly encode dialogue states and transitions. This structure ensures that user interactions follow the intended path, while still allowing users to communicate in a flexible and natural manner. The state graph agent is benchmarked against a monolithic prompt baseline in challenging dialogue scenarios involving ambiguity, incomplete actions, or operator errors. Despite the {LLM} prompt achieving good standalone performance, the state-controlled agent shows greater contextual understanding, reasoning capability, and advisory performance, leading to more intelligent and reliable interactions."
}