Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems

  • Yu-qian Jiang
  • Shi-qi ZhangEmail author
  • Piyush Khandelwal
  • Peter Stone


Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.

Key words

Task planning Robotics Planning domain description language (PDDL) Answer set programming (ASP) 

CLC number



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A portion of this work has taken place in the Learning Agents Research Group (LARG) at UT Austin. LARG research is supported in part by NSF (IIS-1637736, IIS-1651089, IIS-1724157), ONR (N00014-18-2243), FLI (RFP2-000), Intel, Raytheon, and Lockheed Martin. Peter STONE serves on the Board of Directors of Cogitai, Inc. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research.


  1. Babb J, Lee J, 2015. Action language BC+: preliminary report. Proc 29th AAAI Conf on Artificial Intelligence, p.1424–1430.Google Scholar
  2. Calimeri F, Gebser M, Maratea M, et al., 2016. Design and results of the fifth answer set programming competition. Artif Intell, 231:151–181. MathSciNetCrossRefzbMATHGoogle Scholar
  3. Cambon S, Alami R, Gravot F, 2009. A hybrid approach to intricate motion, manipulation and task planning. Int J Robot Res, 28(1):104–126. CrossRefGoogle Scholar
  4. Chen XP, Ji JM, Jiang JQ, et al., 2010. Developing high-level cognitive functions for service robots. Proc 9th Int Conf on Autonomous Agents and Multiagent Systems, p.989–996.Google Scholar
  5. Coles A, Coles A, Olaya AG, et al., 2012. A 6survey of the seventh international planning competition. AI Mag, 33(1):83–88. CrossRefGoogle Scholar
  6. de la Rosa T, Olaya AG, Borrajo D, 2007. Using cases utility for heuristic planning improvement. Int Conf on Case-Based Reasoning, p.137–148.
  7. Erdem E, Patoglu V, 2018. Applications of ASP in robotics. KI-Künstl Intell, 32(2–3):143–149. CrossRefzbMATHGoogle Scholar
  8. Erdem E, Aker E, Patoglu V, 2012. Answer set programming for collaborative housekeeping robotics: representation, reasoning, and execution. Intell Ser Robot, 5(4):275–291. CrossRefGoogle Scholar
  9. Erdem E, Gelfond M, Leone N, 2016. Applications of answer set programming. AI Mag, 37(3):53–58. CrossRefGoogle Scholar
  10. Fawcett C, Vallati M, Hutter F, et al., 2014. Improved features for runtime prediction of domain-independent planners. Proc 24th Int Conf on Automated Planning and Scheduling, p.355–359.Google Scholar
  11. Fikes RE, Nilsson NJ, 1971. Strips: a new approach to the application of theorem proving to problem solving. Artif Intell, 2(3–4):189–208. CrossRefzbMATHGoogle Scholar
  12. Gebser M, Grote T, Schaub T, 2010. Coala: a compiler from action languages to ASP. European Workshop on Logics in Artificial Intelligence, p.360–364.
  13. Gebser M, Kaminski R, Knecht M, et al., 2011. plasp: a prototype for PDDL-based planning in ASP. In: Delgrande JP, Faber W (Eds.), Logic Programming and Nonmonotonic Reasoning. Springer, Berlin, p.358–363. CrossRefGoogle Scholar
  14. Gebser M, Kaminski R, Kaufmann B, et al., 2014. Clingo=ASP+control: preliminary report.
  15. Gelfond M, Kahl Y, 2014. Knowledge Representation, Reasoning, and the Design of Intelligent Agents the Answer-Set Programming Approach. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  16. Gelfond M, Lifschitz V, 1998. Action languages. Electron Trans Artif Intell, 3(6):195–210.MathSciNetGoogle Scholar
  17. Giunchiglia E, Lee J, Lifschitz V, et al., 2004. Nonmonotonic causal theories. Artif Intell, 153(1–2):49–104. MathSciNetCrossRefzbMATHGoogle Scholar
  18. Helmert M, 2006. The fast downward planning system. J Artif Intell Res, 26:191–246. CrossRefzbMATHGoogle Scholar
  19. Helmert M, Röger G, Karpas E, 2011. Fast downward stone soup: a baseline for building planner portfolios. Int Conf on Automated Planning and Scheduling Workshop on Planning and Learning, p.28–35.Google Scholar
  20. Hoffmann J, 2001. FF: the fast-forward planning system. AI Mag, 22(3):57–62.Google Scholar
  21. Khandelwal P, Zhang SQ, Sinapov J, et al., 2017. BWIBots: a platform for bridging the gap between AI and humanrobot interaction research. Int J Robot Res, 36(5–7):635–659. CrossRefGoogle Scholar
  22. Lee J, Lifschitz V, Yang F, 2013. Action language BC: preliminary report. Proc 23rd Int Joint Conf on Artificial Intelligence, p.983–989.Google Scholar
  23. Leyton-Brown K, Nudelman E, Shoham Y, 2002. Learning the empirical hardness of optimization problems: the case of combinatorial auctions. Int Conf on Principles and Practice of Constraint Programming, p.556–572.
  24. Lifschitz V, 1997. Two components of an action language. Ann Math Artif Intell, 21(2–4):305–320. MathSciNetCrossRefzbMATHGoogle Scholar
  25. Lifschitz V, 2002. Answer set programming and plan generation. Artif Intell, 138(1–2):39–54. MathSciNetCrossRefzbMATHGoogle Scholar
  26. Lifschitz V, 2008. What is answer set programming? Proc 23rd National Conf on Artificial Intelligence, p.1594–1597.Google Scholar
  27. Lo SY, Zhang S, Stone P, 2018. PETLON: planning efficiently for task-level-optimal navigation. Proc 17th Conf on Autonomous Agents and Multiagent Systems, p.220–228.Google Scholar
  28. McDermott D, 2003. The formal semantics of processes in PDDL. Proc ICAPS Workshop on PDDL, p.101–155.Google Scholar
  29. McDermott D, Ghallab M, Howe A, et al., 1998. PDDL—the planning domain definition language.
  30. Miura S, Fukunaga A, 2017. Automatic extraction of axioms for planning. Proc 27th Int Conf on Automated Planning and Scheduling, p.218–227.Google Scholar
  31. Richter S, Westphal M, Helmert M, 2011. Lama 2008 and 2011. Int Planning Competition, p.117–124.Google Scholar
  32. Thiébaux S, Hoffmann J, Nebel B, 2005. In defense of PDDL axioms. Artif Intell, 168(1–2):38–69. MathSciNetCrossRefzbMATHGoogle Scholar
  33. Yang F, Khandelwal P, Leonetti M, et al., 2014. Planning in answer set programming while learning action costs for mobile robots. AAAI Spring Symp on Knowledge Representation and Reasoning in Robotics, p. 71–78.Google Scholar
  34. Zhang S, Yang F, Khandelwal P, et al., 2015. Mobile robot planning using action language BC with an abstraction hierarchy. Proc 13th Int Conf on Logic Programming and Nonmonotonic Reasoning, p. 502–516.

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceThe University of Texas at AustinAustinUSA
  2. 2.Department of Computer ScienceThe State University of New York at BinghamtonBinghamtonUSA
  3. 3.Cogitai, Inc.AustinUSA

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