Specifying and achieving goals in open uncertain robot-manipulation domains

This paper describes an integrated solution to the problem of describing and interpreting goals for robots in open uncertain domains. Given a formal specification of a desired situation, in which objects are described only by their properties, general-purpose planning and reasoning tools are used to derive appropriate actions for a robot. These goals are carried out through an online combination of hierarchical planning, state-estimation, and execution that operates robustly in real robot domains with substantial occlusion and sensing error.

[1]  Jerome A. Feldman,et al.  Exploiting deep semantics and compositionality of natural language for Human-Robot-Interaction , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  Leslie Pack Kaelbling,et al.  Integrated task and motion planning in belief space , 2013, Int. J. Robotics Res..

[3]  Matthew R. Walter,et al.  Approaching the Symbol Grounding Problem with Probabilistic Graphical Models , 2011, AI Mag..

[4]  Alessandro Saffiotti,et al.  Perceptual Anchoring of Symbols for Action , 2001, IJCAI.

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

[6]  Leslie Pack Kaelbling,et al.  Belief space planning assuming maximum likelihood observations , 2010, Robotics: Science and Systems.

[7]  Stefanie Tellex,et al.  Clarifying commands with information-theoretic human-robot dialog , 2013, HRI 2013.

[8]  Julian Hough,et al.  It's Not What You Do, It's How You Do It: Grounding Uncertainty for a Simple Robot , 2017, 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI.

[9]  Michael Beetz,et al.  Grounding the Interaction: Anchoring Situated Discourse in Everyday Human-Robot Interaction , 2012, Int. J. Soc. Robotics.

[10]  Amy Loutfi,et al.  An Ontology-Based Symbol Grounding System for Human-Robot Interaction , 2014, AAAI Fall Symposia.

[11]  John D. Kelleher,et al.  Robot perception errors and human resolution strategies in situated human–robot dialogue , 2017, Adv. Robotics.

[12]  Earl D. Sacerdoti,et al.  Planning in a Hierarchy of Abstraction Spaces , 1974, IJCAI.

[13]  Huda Khayrallah,et al.  Natural Language For Human Robot Interaction , 2015 .

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

[15]  Deb Roy,et al.  Probabilistic grounding of situated speech using plan recognition and reference resolution , 2005, ICMI '05.

[16]  Robert C. Moore.  A Formal Theory of Knowledge and Action , 1984 .

[17]  Robert Givan,et al.  FF-Replan: A Baseline for Probabilistic Planning , 2007, ICAPS.

[18]  Moritz Tenorth,et al.  Towards Practical and Grounded Knowledge Representation Systems for Autonomous Household Robots , 2008 .

[19]  Søren Hauberg,et al.  Unscented Kalman Filtering on Riemannian Manifolds , 2013, Journal of Mathematical Imaging and Vision.

[20]  Nikolaos Mavridis,et al.  To ask or to sense? Planning to integrate speech and sensorimotor acts , 2012, 2012 IV International Congress on Ultra Modern Telecommunications and Control Systems.

[21]  Saul A. Kripke,et al.  Naming and Necessity , 1980 .

[22]  Leora Morgenstern,et al.  Knowledge Preconditions for Actions and Plans , 1988, IJCAI.

[23]  Matthias Scheutz,et al.  Resolution of Referential Ambiguity Using Dempster-Shafer Theoretic Pragmatics , 2016, AAAI Fall Symposia.