2019/07/08 by Saeid Amiri, Amiri, Saeid, Sujay Bajracharya +7
Computer Science · #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Multi-Agent Systems and Negotiation #Robotics (cs.RO) #Speech and dialogue systems #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1907.03390
openalex publication_date 2019/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Some robots can interact with humans using natural language, and identify service requests through human-robot dialog. However, few robots are able to improve their language capabilities from this experience. In this paper, we develop a dialog agent for robots that is able to interpret user commands using a semantic parser, while asking clarification questions using a probabilistic dialog manager. This dialog agent is able to augment its knowledge base and improve its language capabilities by learning from dialog experiences, e.g., adding new entities and learning new ways of referring to existing entities. We have extensively evaluated our dialog system in simulation as well as with human participants through MTurk and real-robot platforms. We demonstrate that our dialog agent performs better in efficiency and accuracy in comparison to baseline learning agents. Demo video can be found at https://youtu.be/DFB3jbHBqYE