Towards Closed Feedback Loops in HRI: Integrating InproTK and PaMini

In this paper, we present a first step towards incremental processing for modeling asynchronous human-robot interactions, to allow closed feedback loops in HRI. We achieve this by combining the incremental natural language processing framework InproTK with the human-robot dialog manager PaMini, which is based on generic interaction patterns. This enables the robot to provide incremental feedback during interaction and allows the user to give online feedback and corrections. We provide a first realization scenario as a proof of concept for our approach.

[1]  Etienne de Sevin,et al.  A listener model: introducing personality traits , 2012, Journal on Multimodal User Interfaces.

[2]  David Traum,et al.  The Information State Approach to Dialogue Management , 2003 .

[3]  Gabriel Skantze,et al.  Towards Incremental Speech Generation in Dialogue Systems , 2010, SIGDIAL Conference.

[4]  Sebastian Wrede,et al.  A middleware for collaborative research in experimental robotics , 2011, 2011 IEEE/SICE International Symposium on System Integration (SII).

[5]  Sung-Bae Cho,et al.  A Hierarchical Bayesian Network for Mixed-Initiative Human-Robot Interaction , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[6]  Britta Wrede,et al.  Pamini: A framework for assembling mixed-initiative human-robot interaction from generic interaction patterns , 2010, SIGDIAL Conference.

[7]  付伶俐.  打磨Using Language,倡导新理念 , 2014 .

[8]  Gabriel Skantze,et al.  IrisTK: a statechart-based toolkit for multi-party face-to-face interaction , 2012, ICMI '12.

[9]  Pierre Lison,et al.  Structured Probabilistic Modelling for Dialogue Management , 2014 .

[10]  Nicholas Roy,et al.  Efficient model learning for dialog management , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[11]  Martin Buss,et al.  Information retrieval system for human-robot communication - Asking for directions , 2009, 2009 IEEE International Conference on Robotics and Automation.

[12]  Ingo Lütkebohle,et al.  The bielefeld anthropomorphic robot head “Flobi” , 2010, 2010 IEEE International Conference on Robotics and Automation.

[13]  Stephen T. Wu,et al.  A Framework for Fast Incremental Interpretation during Speech Decoding , 2009, Computational Linguistics.

[14]  J. Glinsky,et al.  The general. , 1982, Nursing.

[15]  David DeVault,et al.  Incremental Dialogue Understanding and Feedback for Multiparty, Multimodal Conversation , 2012, IVA.

[16]  Gabriel Skantze,et al.  A General, Abstract Model of Incremental Dialogue Processing , 2011 .

[17]  David Schlangen,et al.  InproTKs: A Toolkit for Incremental Situated Processing , 2014, SIGDIAL Conference.

[18]  Ellen Campana,et al.  Software architectures for incremental understanding of human speech , 2006, INTERSPEECH.

[19]  Matthias Scheutz,et al.  Incremental natural language processing for HRI , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[20]  David Schlangen,et al.  The InproTK 2012 release , 2012, SDCTD@NAACL-HLT.

[21]  David Schlangen,et al.  Modelling dialogue: Challenges and Aproaches , 2005, Künstliche Intell..

[22]  Udo Hahn,et al.  An Approach to Text Corpus Construction which Cuts Annotation Costs and Maintains Reusability of Annotated Data , 2007, EMNLP.

[23]  Hugh Dubberly,et al.  What is interaction ? Are there different types ? , 2014 .

[24]  Hugh Dubberly,et al.  ON MODELINGWhat is interaction?: are there different types? , 2009, INTR.

[25]  Gabriel Skantze,et al.  A General, Abstract Model of Incremental Dialogue Processing , 2009, EACL.