Skip to main content
SpringerLink
Log in

Is Model-Based Robot Programming a Mirage? A Brief Survey of AI Reasoning in Robotics

  • Technical Contribution
  • Published:
KI - Künstliche Intelligenz Aims and scope Submit manuscript

Abstract

Researchers in AI and Robotics have in common the desire to “make robots intelligent”, evidence of which can be traced back to the earliest AI systems. One major contribution of AI to Robotics is the model-centered approach, whereby intelligence is the result of reasoning in models of the world which can be changed to suit different environments, physical capabilities, and tasks. Dually, robots have contributed to the formulation and resolution of challenging issues in AI, and are constantly eroding the modeling abstractions underlying AI problem solving techniques. Forty-eight years after the first AI-driven robot, this article provides an updated perspective on the successes and challenges which lie at the intersection of AI and Robotics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  1. See http://www.willowgarage.com/pages/pr2/overviewfor an overview of Willow Garage’s PR2.

  2. The basic IA relations are Jointly Exclusive and Pairwise Disjoint (JEPD), meaning that any two intervals are in one and only one relation to each other.

  3. This mechanism is similar in principle to clause learning in SMT [3].

References

  1. Allen J (1984) Towards a general theory of action and time. Artif Intel 23(2):123–154

    Article  MATH  Google Scholar 

  2. Balbiani P, Condotta J-F, Del Cerro LF (1999) A new tractable subclass of the rectangle algebra. In Proc. of 16th Int’l Joint Conf. on Artificial Intelligence (IJCAI), vol 1, pp 442–447

  3. Barrett C, Sebastiani R, Seshia SA, Tinelli C (2009) Satisfiability modulo theories. In A Biere, MJH Heule, H van Maaren, T Walsh (Eds), Handbook of satisfiability, ch 26, IOS Press, pp 825–885

  4. Bhatia A, Maly MR, Kavraki LE, Vardi MY (2011) Motion planning with complex goals. Robot Autom Mag, IEEE, 18(3):55–64

    Article  Google Scholar 

  5. Bohlken W, Neumann B, Hotz L, Koopmann P (2011) Ontology-based realtime activity monitoring using beam search. In Proc. of 8th Int’l Conf. on Computer Vision Systems

  6. Bresina JL, Jónsson AK, Morris PH, Rajan K ( 2005) Activity planning for the mars exploration rovers. In Proc. of 15th Int’l Conf. on Automated Planning and Scheduling (ICAPS)

  7. Cambon S, Alami R, Gravot F (2009) A hybrid approach to intricate motion, manipulation and task planning. Int’l J Robot Res 28:104–126

    Article  Google Scholar 

  8. Cesta A, Oddi A, Smith SF (2002) A constraint-based method for project scheduling with time windows. J Heuristics 8(1):109–136

    Article  MATH  Google Scholar 

  9. Cohn AG, Renz J, Sridhar M (2012) Thinking inside the box: A comprehensive spatial representation for video analysis. In Proc. of 13th Int’l Conf. on Principles of Knowledge Representation and Reasoning

  10. Colliot O, Camara O, Bloch I (2006) Integration of fuzzy spatial relations in deformable models-application to brain mri segmentation. Pattern Recogn 39(8):1401–1414

    Article  Google Scholar 

  11. de Moura L, Dutertre B, Shankar N (2007) A tutorial on satisfiability modulo theories. In Proc. of 19th Int’l Conf. on Computer Aided Verification

  12. de Silva L, Pandey AK, Alami R (2013) An interface for interleaved symbolic-geometric planning and backtracking. In IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems

  13. Dechter R, Meiri I, Pearl J (1991) Temporal constraint networks. Artif Intel 49(1–3):61–95

    Article  MathSciNet  MATH  Google Scholar 

  14. Di Rocco M, Pecora F, Saffiotti A (2013) When robots are late: Configuration planning for multiple robots with dynamic goals. In Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems

  15. Doherty P, Kvarnström J, Heintz F (2010) A temproal logic-based planning and execution monitoring framework for unmanned aircraft systems. J Autom Agents Multi-Agent Sys, 2(2)

  16. Dornhege C, Eyerich P, Keller T, Trüg S, Brenner M, Nebel B (2009) Semantic attachments for domain-independent planning systems. In Proc. of 19th Int’l Conf. on Automated Planning and Scheduling (ICAPS), pp 114–121. AAAI Press

  17. Drakengren T, Jonsson P (1998) A complete classification of tractability in allen’s algebra relative to subsets of basic relations. Artif Intel 106(2):205–219

    Article  MathSciNet  MATH  Google Scholar 

  18. Erol K, Hendler J, Nau DS (1994) HTN Planning: Complexity and expressivity. In Proc. of 12th National Conf. on Artificial Intelligence

  19. Fikes RE, Hart PE, Nilsson NJ (1972) Learning and executing generalized robot plans. Artif Intel 3:251–288

    Article  Google Scholar 

  20. Fikes RE, Nilsson NJ (1971) strips: A new approach to theorem proving in problem solving. Artif Intell 2:189–208

    Article  MATH  Google Scholar 

  21. Fratini S, Pecora F, Cesta A (2008) Unifying planning and scheduling as timelines in a component-based perspective. Arch Control Sci 18(2):231–271

    MathSciNet  MATH  Google Scholar 

  22. Gaschler A, Petrick RPA, Giuliani M, Rickert M, Knoll A (2013) Kvp: A knowledge of volumes approach to robot task planning. In Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems

  23. Ghallab M, Nau DS, Traverso P (2004) Automated Planning: Theory and Practice. Morgan Kaufmann

  24. Gregory P, Long D, Fox M, Beck JC (2012) Planning modulo theories: extending the planning paradigm. In Proc. of Int. Conf. on Automated Planning and Scheduling (ICAPS)

  25. Havur G, Ozbilgin G, Erdem E, Patoglu V (2014) Geometric rearrangement of multiple movable objects on cluttered surfaces: A hybrid reasoning approach. In Proc. of IEEE Int’l Conf. on Robotics and Automation

  26. Jónsson AK, Morris PH, Muscettola N, Rajan K, Smith B (2000) Planning in interplanetary space: Theory and practice. In Int. Conf. on Automated Planning and Scheduling, pp 177–186

  27. Jonsson P, Krokhin A (2004) Complexity classification in qualitative temporal constraint reasoning. Artif Intel 160(1–2):35–51

    Article  MathSciNet  MATH  Google Scholar 

  28. Kaelbling LP, Lozano-Pérez T (2011) Hierarchical planning in the now. In Proc. of IEEE Conf. on Robotics and Automation

  29. Kaelbling LP, Lozano-Pérez. T (2012) Unifying perception, estimation and action for mobile manipulation via belief space planning. In Proc. of IEEE Conf. on Robotics and Automation

  30. Köckemann U, Karlsson L, Pecora F (2014) Grandpa hates robots—interaction constraints for planning in inhabited environments. In Proc. of AAAI Conf. on Artificial Intelligence

  31. Kupferman O, Vardi MY (2001) Model checking of safety properties. Formal Methods Sys Design 19(3):291–314

    Article  MathSciNet  MATH  Google Scholar 

  32. Lagriffoul F, Dimitrov D, Saffiotti A, Karlsson L (2012) Constraint propagation on interval bounds for dealing with geometric backtracking. In Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems

  33. Lifschitz V (2008) What is answer set programming? In Proc. of 23rd AAAI Conf. on Artificial Intelligence, pp 1594–1597

  34. Loutfi A, Coradeschi S, Daoutis M, Melchert J (2008) Using knowledge representation for perceptual anchoring in a robotic system. Int’l J Artif Intel Tools 17(5):925–944

    Article  Google Scholar 

  35. Mansouri M, Pecora F (2013) A representation for spatial reasoning in robotic planning. In Proc. of IROS Workshop on AI-based Robotics

  36. Mansouri M, Pecora F (2014) More knowledge on the table: planning with space, time and resources for robots. In Proc. of IEEE Int’l Conf. on Robotics and Automation

  37. McGann C, Py F, Rajan K, Ryan J, Henthorn R (2008) Adaptive control for autonomous underwater vehicles. In Proc. of 23rd National Conf. on Artificial Intelligence

  38. Moffitt MD, Pollack ME (2006) Optimal rectangle packing: A meta-CSP approach. In Proc. of 16th Int’l Conf. on Automated Planning and Scheduling (ICAPS)

  39. Mösenlechner L, Beetz M (2011) Parameterizing actions to have the appropriate effects. In Proc. of IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems

  40. Mösenlechner L, Beetz M (2013) Fast temporal projection using accurate physics-based geometric reasoning. In Proc. of IEEE Int’l Conf. on Robotics and Automation

  41. Nedunuri S, Prabhu S, Moll M, Chaudhuri S, Kavraki LE (2014) SMT-based synthesis of integrated task and motion plans for mobile manipulation. In Proc. of IEEE Int’l Conf. on Robotics and Automation

  42. Pecora F, Cirillo M, Dell’Osa F, Ullberg J, Saffiotti A (2012) A constraint-based approach for proactive, context-aware human support. J Ambient Intel Smart Environ 4(4):347–367

    Google Scholar 

  43. Pecora F, Cirillo M, Dimitrov D (2012) On mission-dependent coordination of multiple vehicles under spatial and temporal constraints. In IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems

  44. Petrick RPA, Bacchus F (2004) Extending the knowledge-based approach to planning with incomplete information and sensing. In Proc. of 14th Int’l Conf. on Automated Planning and Scheduling (ICAPS)

  45. Plaku E, Kavraki LE, Vardi MY (2009) Hybrid systems: from verification to falsification by combining motion planning and discrete search. Formal Methods Sys Design 34:157–182

    Article  MATH  Google Scholar 

  46. Pnueli A (1977) The temporal logic of programs. In Proc. of 18th Annual Symposium on Foundations of Computer Science, pp 46–57

  47. Quigley M, Conley K, Gerkey BP, Faust J, Foote T, Leibs J, Wheeler R, Ng AY (2009) ROS: an open-source Robot Operating System. In Proc. of ICRA Workshop on Open Source Software

  48. Randell DA, Cui Z, Cohn AG (1992) A spatial logic based on regions and connection. In Proc. of Int’l Conf. on Principles of Knowledge Representation and Reasoning (KR)

  49. Renz J, Nebel B ( 2007) Qualitative spatial reasoning using constraint calculi. In Aiello M, Pratt-Hartmann I, van Benthem J (Eds), Handbook of spatial logics, pages 161–215. Springer

  50. Skiadopoulos S, Koubarakis M (2004) Composing cardinal direction relations. Artif Intell 152(2):143–171

    Article  MathSciNet  MATH  Google Scholar 

  51. Tsang EPK (1993) Foundations of Constraint Satisfaction. Academic Press, London and San Diego

    Google Scholar 

  52. Vidal T, Fargier H (1999) Handling contingency in temporal constraint networks: from consistency to controllabilities. J Exp Theor Artif Intel 11:23–45

    Article  MATH  Google Scholar 

  53. Vilain M, Kautz H, van Beek P (1990) Constraint propagation algorithms for temporal reasoning: A revised report. In D.S. Weld and J. de Kleer, editors, Readings in Qualitative Reasoning About Physical Systems, pages 373–381. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA

  54. Wang X, Keller JM, Gader P (1997) Using spatial relationships as features in object recognition. In Annual Meeting of the North American Fuzzy Information Processing Society

  55. Williams BC, Ingham MD, Chung SH, Elliott PH (2003) Model-based programming of intelligent embedded systems and robotic space explorers. Proc IEEE 91(1):212–237

    Article  Google Scholar 

Download references

Acknowledgments

The Author wishes to thank Štefan Konečný, Masoumeh Mansouri and Alessandro Saffiotti for the many discussions and comments that have shaped the positions stated in this article, as well as Joachim Hertzberg, Fabien Lagriffoul and Benjamin Andres for useful comments on the text. This work is partially supported by EU-FP7 project RACE (grant no. 287752), and by Swedish Knowledge Foundation (KKS) project “Semantic Robot”.

Author information

Authors and Affiliations

  1. Center for Applied Autonomous Sensor Systems, Örebro University, 70182, Örebro, Sweden

    Federico Pecora

Authors
  1. Federico Pecora
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Federico Pecora.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pecora, F. Is Model-Based Robot Programming a Mirage? A Brief Survey of AI Reasoning in Robotics. Künstl Intell 28, 255–261 (2014). https://doi.org/10.1007/s13218-014-0325-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13218-014-0325-0

Keywords

Search

Navigation