Learning to understand questions on the task history of a service robot
Vittorio Perera, Manuela Veloso · 2017
We present a novel approach to enable a mobile service robot to understand questions about the history of tasks it has executed. We frame the problem of understanding such questions as grounding an input sentence to a query that can be executed on the logs recorded by the robot during its runs. We define a query as an operation followed by a set of filters. In order to ground sentence to a query we introduce a joint probabilistic model. The model is composed by a shallow semantic parser and a knowledge base to store and re-use the groundings of a sentence. The Knowledge Base and its predicates are designed to match the structure of a query. Our results show that, by using such Knowledge Base, the approach proposed requires fewer and fewer corrections as users interact with the system.