Title :
Everything robots always wanted to know about housework (but were afraid to ask)
Author :
Nyga, Daniel ; Beetz, Michael
Author_Institution :
Intell. Autonomous Syst. Group, Tech. Univ. Munchen, München, Germany
Abstract :
In this paper we discuss the problem of action-specific knowledge processing, representation and acquisition by autonomous robots performing everyday activities. We report on a thorough analysis of the household domain, which has been performed on a large corpus of natural-language instructions from the Web and underlines the supreme need of action-specific knowledge for robots acting in those environments. We introduce the concept of Probabilistic Robot Action Cores (PRAC) that are well-suited for encoding such knowledge in a probabilistic first-order knowledge base. We additionally show how such a knowledge base can be acquired by natural language and we address the problems of incompleteness, underspecification and ambiguity of naturalistic action specifications and point out how PRAC models can tackle those.
Keywords :
Internet; control engineering computing; human-robot interaction; natural language processing; probability; service robots; PRAC models; Web; action-specific knowledge processing; autonomous robots; household domain; housework; natural-language instructions; probabilistic first-order knowledge base; probabilistic robot action cores; Abstracts; Context; Humans; Knowledge based systems; Natural languages; Probabilistic logic; Robots;
Conference_Titel :
Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on
Conference_Location :
Vilamoura
Print_ISBN :
978-1-4673-1737-5
DOI :
10.1109/IROS.2012.6385923