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

arXiv: https://arxiv.org/abs/1907.03390 · HTML: https://mlsanity.com/paper/1907.03390/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds](https://mlsanity.com/paper/2305.17590/) (2023)
- [Robot Task Planning and Situation Handling in Open Worlds](https://mlsanity.com/paper/2210.01287/) (2022)
- [Language Models can Infer Action Semantics for Symbolic Planners from Environment Feedback](https://mlsanity.com/paper/2406.02791/) (2024)
- [Survey of Design Paradigms for Social Robots](https://mlsanity.com/paper/2407.20556/) (2024)
- [Learning Visualization Policies of Augmented Reality for Human-Robot Collaboration](https://mlsanity.com/paper/2211.07028/) (2022)

## Abstract (arXiv)

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