Novel Mechanisms for Natural Human-Robot Interactions in the DIARC Architecture

Natural human-like human-robot interactions require many functional capabilities from a robot that have to be reflected in architectural components in the robotic control architecture. In particular, various mechanisms for producing social behaviors, goal-oriented cognition, and robust intelligence are required. In this paper, we present an overview of the most recent version of our DIARC architecture and show how several novel algorithms attempt to address these three areas, leading to more natural interactions with humans, while also extending the overall capability of the integrated system.

[1]  C. Raymond Perrault,et al.  A Plan-Based Analysis of Indirect Speech Act , 1980, CL.

[2]  Matthias Scheutz,et al.  Toward Affective Cognitive Robots for Human-Robot Interaction , 2005, AAAI.

[3]  Matthias Scheutz,et al.  ADE: A Framework for Robust Complex Robotic Architectures , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Geert-Jan M. Kruijff,et al.  Service Robots Dealing with Indirect Speech Acts , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Matthias Scheutz,et al.  ADE - Steps Towards a Distributed Development and Runtime Environment for Complex Robotic Agent Architectures , 2006 .

[6]  Matthias Scheutz,et al.  The utility of affect expression in natural language interactions in joint human-robot tasks , 2006, HRI '06.

[7]  William Whittaker,et al.  Robotic introspection for exploration and mapping of subterranean environments , 2007 .

[8]  Matthias Scheutz,et al.  First steps toward natural human-like HRI , 2007, Auton. Robots.

[9]  Marilyn A. Walker,et al.  How Rude Are You?: Evaluating Politeness and Affect in Interaction , 2007, ACII.

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

[11]  Matthias Scheutz,et al.  Reflection and Reasoning Mechanisms for Failure Detection and Recovery in a Distributed Robotic Architecture for Complex Robots , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[12]  Matthias Scheutz,et al.  Development environments for autonomous mobile robots: A survey , 2007, Auton. Robots.

[13]  Richard N. Taylor,et al.  Policy-based self-adaptive architectures: a feasibility study in the robotics domain , 2008, SEAMS '08.

[14]  Charles R. Crowell,et al.  Robot social presence and gender: Do females view robots differently than males? , 2008, 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[15]  Jeff Magee,et al.  From goals to components: a combined approach to self-management , 2008, SEAMS '08.

[16]  Matthias Scheutz,et al.  Dynamic robot autonomy: investigating the effects of robot decision-making in a human-robot team task , 2009, ICMI-MLMI '09.

[17]  Matthias Scheutz,et al.  What to do and how to do it: Translating natural language directives into temporal and dynamic logic representation for goal management and action execution , 2009, 2009 IEEE International Conference on Robotics and Automation.

[18]  Matthias Scheutz,et al.  Affective Goal and Task Selection for Social Robots , 2009 .

[19]  Matthias Scheutz,et al.  The Utility of Affect in the Selection of Actions and Goals Under Real-World Constraints , 2009, IC-AI.

[20]  Charles R. Crowelly,et al.  Gendered voice and robot entities: Perceptions and reactions of male and female subjects , 2009 .

[21]  Matthias Scheutz,et al.  Planning for human-robot teaming in open worlds , 2010, TIST.

[22]  Tim Oates,et al.  The Metacognitive Loop: An Architecture for Building Robust Intelligent Systems , 2010, AAAI Fall Symposium: Commonsense Knowledge.

[23]  Dieter Fox,et al.  Following directions using statistical machine translation , 2010, HRI 2010.

[24]  Matthias Scheutz,et al.  Robust spoken instruction understanding for HRI , 2010, HRI 2010.

[25]  Matthias Scheutz,et al.  Investigating multimodal real-time patterns of joint attention in an hri word learning task , 2010, HRI 2010.

[26]  Matthias Scheutz,et al.  Disentangling the Effects of Robot Affect, Embodiment, and Autonomy on Human Team Members in a Mixed-Initiative Task , 2011, ACHI 2011.

[27]  Matthias Scheutz,et al.  Toward Humanlike Task-Based Dialogue Processing for Human Robot Interaction , 2011, AI Mag..

[28]  Matthias Scheutz,et al.  Learning actions from human-robot dialogues , 2011, 2011 RO-MAN.

[29]  Matthias Scheutz,et al.  Facilitating Mental Modeling in Collaborative Human-Robot Interaction through Adverbial Cues , 2011, SIGDIAL Conference.

[30]  Matthias Scheutz,et al.  Incremental Referent Grounding with NLP-Biased Visual Search , 2012 .

[31]  Matthias Scheutz,et al.  Crossing Boundaries: Multi-Level Introspection in a Complex Robotic Architecture for Automatic Performance Improvements , 2012, AAAI.

[32]  Matthias Scheutz,et al.  Adaptive eye gaze patterns in interactions with human and artificial agents , 2012, TIIS.

[33]  Matthias Scheutz,et al.  Investigating the Effects of Robotic Displays of Protest and Distress , 2012, ICSR.

[34]  Matthias Scheutz,et al.  Abstract planning for reactive robots , 2012, 2012 IEEE International Conference on Robotics and Automation.

[35]  Matthias Scheutz,et al.  Multi-modal Belief Updates in Multi-Robot Human-Robot Dialogue Interactions , 2012 .

[36]  Matthias Scheutz,et al.  Tell me when and why to do it! Run-time planner model updates via natural language instruction , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[37]  Matthias Scheutz,et al.  Neural Circuits for Any-Time Phrase Recognition with Applications in Cognitive Models and Human-Robot Interaction , 2012, CogSci.

[38]  Matthias Scheutz,et al.  Incrementally biasing visual search using natural language input , 2013, AAMAS.

[39]  Matthias Scheutz,et al.  A Hybrid Architectural Approach to Understanding and Appropriately Generating Indirect Speech Acts , 2013, AAAI.

[40]  Matthias Scheutz,et al.  Linking Cognitive Tokens to Biological Signals: Dialogue Context Improves Neural Speech Recognizer Performance , 2013, CogSci.

[41]  Matthias Scheutz,et al.  Grounding Natural Language References to Unvisited and Hypothetical Locations , 2013, AAAI.