Learning Task Knowledge from Dialog and Web Access

Robotics

Vittorio Perera⁵  Robin Soetens³  Thomas Kollar⁵  Mehdi Samadi⁵  Yichao Sun²  Daniele Nardi⁴  René van de Molengraft³  Manuela Veloso⁵  Nicola Bellotto¹  Nick Hawes¹ 

[1] School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;;State Key Laboratory of Industrial Control Technology, Zhejiang University, 38 Zheda Road, Hangzhou 456555, China; E-Mail:;Department of Mechanical Engineering, Eindhoven University of Technology, Den Dolech 2, Eindhoven; E-Mails:;Department of Computer, Control, and Management Engineering “Antonio Ruberti”, “Sapienza” University of Rome Via Ariosto 25, Rome 00185, Italy; E-Mail:;School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA; E-Mails:

关键词: knowledge acquisition;    knowledge based systems;    knowledge transfer;    robots;    intelligent robots;    service robots;    mobile robots;    human robot interaction;    speech;    speech recognition;   

DOI  :  10.3390/robotics4020223

来源: mdpi

【 摘 要 】

We present KnoWDiaL, an approach for Learning and using task-relevant Knowledge from human-robot Dialog and access to the Web. KnoWDiaL assumes that there is an autonomous agent that performs tasks, as requested by humans through speech. The agent needs to “understand” the request, (i.e., to fully ground the task until it can proceed to plan for and execute it). KnoWDiaL contributes such understanding by using and updating a Knowledge Base, by dialoguing with the user, and by accessing the web. We believe that KnoWDiaL, as we present it, can be applied to general autonomous agents. However, we focus on our work with our autonomous collaborative robot, CoBot, which executes service tasks in a building, moving around and transporting objects between locations. Hence, the knowledge acquired and accessed consists of groundings of language to robot actions, and building locations, persons, and objects. KnoWDiaL handles the interpretation of voice commands, is robust regarding speech recognition errors, and is able to learn commands involving referring expressions in an open domain, (i.e., without requiring a lexicon). We present in detail the multiple components of KnoWDiaL, namely a frame-semantic parser, a probabilistic grounding model, a web-based predicate evaluator, a dialog manager, and the weighted predicate-based Knowledge Base. We illustrate the knowledge access and updates from the dialog and Web access, through detailed and complete examples. We further evaluate the correctness of the predicate instances learned into the Knowledge Base, and show the increase in dialog efficiency as a function of the number of interactions. We have extensively and successfully used KnoWDiaL in CoBot dialoguing and accessing the Web, and extract a few corresponding example sequences from captured videos.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.