Augmenting Knowledge through Statistical, Goal-oriented Human-Robot Dialog

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

Authors

• Saeid Amiri
• Sujay Bajracharya
• Cihangir Goktolga
• Jesse Thomason
• Shiqi Zhang 0001

Keywords

• Accuracy
• Semantics
• Natural languages
• Knowledge based systems
• Probabilistic logic
• Intelligent robots
• Semantic
• Knowledge Base
• Natural Language
• Human Participants
• Service Requests
• Dialogue System
• Language Capabilities
• Uniform Distribution
• F1 Score
• Simulation Experiments
• Knowledge Management
• Termination Condition
• Markov Decision Process
• Mobile Robot
• Language Understanding
• Human-robot Interaction
• Human Intention
• Human Users
• Robot Operating System
• Service Robots
• As-needed Basis