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Manipulation of Articulated Objects Using Dual-arm Robots via Answer Set Programming

Published online by Cambridge University Press:  14 December 2020

RICCARDO BERTOLUCCI ,
ALESSIO CAPITANELLI ,
CARMINE DODARO Open the ORCID record for CARMINE DODARO [Opens in a new window] ,
NICOLA LEONE ,
MARCO MARATEA Open the ORCID record for MARCO MARATEA [Opens in a new window] ,
FULVIO MASTROGIOVANNI  and
MAURO VALLATI
RICCARDO BERTOLUCCI
Affiliation:
University of Calabria, Italy (e-mail: bertolucci@mat.unical.it)
ALESSIO CAPITANELLI
Affiliation:
Teseo srl, Italy (e-mail: alessio.capitanelli@teseotech.com)
CARMINE DODARO
Affiliation:
University of Calabria, Italy (e-mails: dodaro@mat.unical.it, leone@mat.unical.it)
NICOLA LEONE
Affiliation:
University of Calabria, Italy (e-mails: dodaro@mat.unical.it, leone@mat.unical.it)
MARCO MARATEA
Affiliation:
University of Genoa, Italy (e-mails: marco.maratea@unige.it, fulvio.mastrogiovanni@unige.it)
FULVIO MASTROGIOVANNI
Affiliation:
University of Genoa, Italy (e-mails: marco.maratea@unige.it, fulvio.mastrogiovanni@unige.it)
MAURO VALLATI
Affiliation:
University of Huddersfield, UK (e-mail:m.vallati@hud.ac.uk)
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Abstract

The manipulation of articulated objects is of primary importance in Robotics and can be considered as one of the most complex manipulation tasks. Traditionally, this problem has been tackled by developing ad hoc approaches, which lack flexibility and portability. In this paper, we present a framework based on answer set programming (ASP) for the automated manipulation of articulated objects in a robot control architecture. In particular, ASP is employed for representing the configuration of the articulated object for checking the consistency of such representation in the knowledge base and for generating the sequence of manipulation actions. The framework is exemplified and validated on the Baxter dual-arm manipulator in the first, simple scenario. Then, we extend such scenario to improve the overall setup accuracy and to introduce a few constraints in robot actions execution to enforce their feasibility. The extended scenario entails a high number of possible actions that can be fruitfully combined together. Therefore, we exploit macro actions from automated planning in order to provide more effective plans. We validate the overall framework in the extended scenario, thereby confirming the applicability of ASP also in more realistic Robotics settings and showing the usefulness of macro actions for the robot-based manipulation of articulated objects.

Keywords

answer set programmingrobots manipulationmacro actions
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 21 , Issue 3 , May 2021 , pp. 372 - 401
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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References

Alviano, M., Amendola, G., Dodaro, C., Leone, N., Maratea, M. and Ricca, F. 2019. Evaluation of disjunctive programs in WASP. In LPNMR, Balduccini, M., Lierler, Y., and Woltran, S., Eds. Lecture Notes in Computer Science, vol. 11481. Springer, 241–255.Google Scholar
Andres, B., Rajaratnam, D., Sabuncu, O. and Schaub, T. 2015. Integrating ASP into ROS for reasoning in robots. In Proceedings of the 13th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2015), Calimeri, F., Ianni, G. and Truszczynski, M., Eds. Lecture Notes in Computer Science, vol. 9345. Springer, 69–82.CrossRefGoogle Scholar
Balduccini, M. and Lierler, Y. 2017. Constraint answer set solver EZCSP and why integration schemas matter. Theory and Practice of Logic Programming 17, 4, 462–515.CrossRefGoogle Scholar
Banbara, M., Kaufmann, B., Ostrowski, M. and Schaub, T. 2017. Clingcon: The next generation. Theory and Practice of Logic Programming 17, 4, 408–461.CrossRefGoogle Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press.CrossRefGoogle Scholar
Bertolucci, R., Capitanelli, A., Dodaro, C., Leone, N., Maratea, M., Mastrogiovanni, F. and Vallati, M. 2019. An ASP-based framework for the manipulation of articulated objects using dual-arm robots. In Proceedings of the 15th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2019), Balduccini, M., Lierler, Y., and Woltran, S., Eds. Lecture Notes in Computer Science, vol. 11481. Springer, 32–44.CrossRefGoogle Scholar
Bodenhagen, L., Fugl, A. R., Jordt, A., Willatzen, M., Andersen, K. A., Olsen, M. M., Koch, R., Petersen, H. G. and Krüger, N. 2014. An adaptable robot vision system performing manipulation actions with flexible objects. IEEE Transactions on Automation Science and Engineering 11, 3, 749–765.CrossRefGoogle Scholar
Botea, A., Enzenberger, M., Müller, M. and Schaeffer, J. 2005. Macro-FF: improving AI planning with automatically learned macro-operators. Journal of Artificial Intelligence Research 24, 581–621.CrossRefGoogle Scholar
Brewka, G., Eiter, T. and Truszczynski, M. 2011. Answer set programming at a glance. Communications of the ACM 54, 12, 92–103.CrossRefGoogle Scholar
Cabalar, P., Kaminski, R., Morkisch, P. and Schaub, T. 2019. telingo = ASP + time. In Proceedings of the 15th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2019), Balduccini, M., Lierler, Y., and Woltran, S., Eds. Lecture Notes in Computer Science, vol. 11481. Springer, 256–269.CrossRefGoogle Scholar
Calimeri, F., Faber, W., Gebser, M., Ianni, G., Kaminski, R., Krennwallner, T., Leone, N., Maratea, M., Ricca, F. and Schaub, T. 2020. ASP-core-2 input language format. Theory and Practice of Logic Programming 20, 2, 294–309.CrossRefGoogle Scholar
Capitanelli, A., Maratea, M., Mastrogiovanni, F. and Vallati, M. 2017. Automated planning techniques for robot manipulation tasks involving articulated objects. In Proceedings of the 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017), Esposito, F., Basili, R., Ferilli, S., and Lisi, F. A., Eds. Lecture Notes in Computer Science, vol. 10640. Springer, 483–497.Google Scholar
Capitanelli, A., Maratea, M., Mastrogiovanni, F. and Vallati, M. 2018. On the manipulation of articulated objects in human-robot cooperation scenarios. Robotics and Autonomous Systems 109, 139–155.CrossRefGoogle Scholar
Chrpa, L. 2010. Generation of macro-operators via investigation of action dependencies in plans. Knowledge Engineering Review 25, 3, 281–297.CrossRefGoogle Scholar
Chrpa, L. and Vallati, M. 2019. Improving domain-independent planning via critical section macro-operators. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019). AAAI Press, 7546–7553.Google Scholar
Chrpa, L., Vallati, M. and McCluskey, T. L. 2015. On the online generation of effective macro-operators. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015). AAAI Press, 1544–1550.Google Scholar
Coles, A., Fox, M. and Smith, A. 2007. Online identification of useful macro-actions for planning. In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS 2007), Boddy, M. S., Fox, M., and Thiébaux, S., Eds. AAAI, 97–104.Google Scholar
Darvish, K., Wanderlingh, F., Bruno, B., Simetti, E., Mastrogiovanni, F. and Casalino, G. 2018. Flexible human-robot cooperation models for assisted show-floor tasks. Mechatronics 51, 97–114.CrossRefGoogle Scholar
Di Rosa, E., Giunchiglia, E. and Maratea, M. 2008. A new approach for solving satisfiability problems with qualitative preferences. In ECAI, Ghallab, M., Spyropoulos, C. D., Fakotakis, N., and Avouris, N. M., Eds. Frontiers in Artificial Intelligence and Applications, vol. 178. IOS Press, 510–514.Google Scholar
Di Rosa, E., Giunchiglia, E. and Maratea, M. 2010. Solving satisfiability problems with preferences. Constraints 15, 4, 485–515.CrossRefGoogle Scholar
Dimopoulos, Y., Gebser, M., Lühne, P., Romero, J. and Schaub, T. 2017. plasp 3: Towards effective ASP planning. In Proceedings of the 14th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2017), Balduccini, M. and Janhunen, T., Eds. Lecture Notes in Computer Science, vol. 10377. Springer, 286–300.CrossRefGoogle Scholar
Erdem, E. and Patoglu, V. 2018. Applications of ASP in robotics. Künstliche Intelligenz 32, 2–3, 143–149.CrossRefGoogle Scholar
Erdem, E., Patoglu, V. and Saribatur, Z. G. 2015. Integrating hybrid diagnostic reasoning in plan execution monitoring for cognitive factories with multiple robots. In Proceedings of the IEEE International Conference on Robotics and Automation, (ICRA 2015). IEEE, 2007–2013.Google Scholar
Erdem, E., Patoglu, V., Saribatur, Z. G., Schüller, P., and Uras, T. 2013. Finding optimal plans for multiple teams of robots through a mediator: A logic-based approach. Theory and Practice of Logic Programming 13, 4–5, 831–846.CrossRefGoogle Scholar
Gebser, M., Jost, H., Kaminski, R., Obermeier, P., Sabuncu, O., Schaub, T. and Schneider, M. 2013. Ricochet robots: A transverse ASP benchmark. In Proceedings of the 12th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2013), Cabalar, P. and Son, T. C., Eds. Lecture Notes in Computer Science, vol. 8148. Springer, 348–360.CrossRefGoogle Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Wanko, P. 2016. Theory solving made easy with clingo 5. In Proceedings of the 32rd Technical Communications of the International Conference on Logic Programming (ICLP 2016 TCs), Carro, M., King, A., Saeedloei, N., and Vos, M. D., Eds. OASICS, vol. 52. Schloss Dagstuhl, 2:1–2:15.Google Scholar
Gebser, M., Maratea, M. and Ricca, F. 2017. The design of the seventh answer set programming competition. In LPNMR, Balduccini, M. and Janhunen, T., Eds. Lecture Notes in Computer Science, vol. 10377. Springer, 3–9.Google Scholar
Gebser, M., Maratea, M. and Ricca, F. 2020. The seventh answer set programming competition: Design and results. Theory and Practice of Logic Programming 20, 2, 176–204.CrossRefGoogle Scholar
Gelfond, M. and Lifschitz, V. 1988. The stable model semantics for logic programming. In Proceedings of 5th the International Conference on Logic Programming (ICLP 1988). MIT Press, 1070–1080.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 365–385.CrossRefGoogle Scholar
Gerevini, A. E., Saetti, A. and Vallati, M. 2015. Exploiting macro-actions and predicting plan length in planning as satisfiability. AI Communications 28, 2, 323–344.CrossRefGoogle Scholar
Giunchiglia, E. and Maratea, M. 2006. Solving optimization problems with DLL. In Proceedings of the 17th European Conference on Artificial Intelligence (ECAI 2006). Frontiers in Artificial Intelligence and Applications, vol. 141. IOS Press, 377–381.Google Scholar
Harnad, S. 1990. The symbol grounding problem. Physica D 42, 335–346.CrossRefGoogle Scholar
Heyer, C. 2010. Human-Robot interaction and future industrial robotics applications. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 4749–4754.Google Scholar
Hoffmann, J. and Nebel, B. 2001. The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302.CrossRefGoogle Scholar
Howey, R., Long, D. and Fox, M. 2004. VAL: automatic plan validation, continuous effects and mixed initiative planning using PDDL. In Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004). IEEE Computer Society, 294–301.Google Scholar
Kautz, H. A. and Selman, B. 1992. Planning as satisfiability. In Proceedings of the 10th European Conference on Artificial Intelligence (ECAI 1992), Neumann, B., Ed. John Wiley and Sons, 359–363.Google Scholar
Korf, R. 1985. Macro-Operators: A weak method for learning. Artificial Intelligence 26, 1, 35–77.CrossRefGoogle Scholar
Krüger, J., Lien, T. K. and Verl, A. 2009. Cooperation of human and machines in assembly lines. CIRP Annals 58, 2, 628–646.CrossRefGoogle Scholar
Lifschitz, V. 2002. Answer set programming and plan generation. Artificial Intelligence 138, 1–2, 39–54.CrossRefGoogle Scholar
McCluskey, T. L. and Porteous, J. M. 1997. Engineering and compiling planning domain models to promote validity and efficiency. Artificial Intelligence 95, 1, 1–65.CrossRefGoogle Scholar
Nair, A., Chen, D., Agrawal, P., Isola, P., Abbeel, P., Malik, J. and Levine, S. 2017. Combining self-supervised learning and imitation for vision-based rope manipulation. In Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA 2017). IEEE, 2146–2153.CrossRefGoogle Scholar
Newton, M. A. H., Levine, J., Fox, M. and Long, D. 2007. Learning macro-actions for arbitrary planners and domains. In Proceedings of the 17th International Conference on Automated Planning and Scheduling (ICAPS 2007), Boddy, M. S., Fox, M., and Thiébaux, S., Eds. AAAI, 256–263.Google Scholar
Niemelä, I. 1999. Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25, 3–4, 241–273.CrossRefGoogle Scholar
Schäpers, B., Niemueller, T., Lakemeyer, G., Gebser, M. and Schaub, T. 2018. ASP-Based Time-Bounded Planning for Logistics Robots. In Proceedings of the 28th International Conference on Automated Planning and Scheduling (ICAPS 2018). AAAI Press, 509–517.Google Scholar
Schulman, J., Ho, J., Lee, C. and Abbeel, P. 2013. Learning from demonstrations through the use of non-rigid registration. In Proceedings of the 16th International Symposium on Robotics Research (ISRR 2013), Inaba, M. and Corke, P., Eds. Springer Tracts in Advanced Robotics, vol. 114. Springer, 339–354.Google Scholar
Shen, D. and Lierler, Y. 2018. Smt-Based constraint answer set solver EZSMT+ for non-tight programs. In Proceedings of the 16th International Conference on Principles of Knowledge Representation and Reasoning (KR 2018), Thielscher, M., Toni, F., and Wolter, F., Eds. AAAI Press, 67–71.Google Scholar
Thomas, A., Amatya, S., Mastrogiovanni, F. and Baglietto, M. 2018. Towards perception-aware task-motion planning. In Proceedings of the AAAI 2018 Fall Symposium on Reasoning and Learning in Real-World Systems for Long-Term Autonomy. Arlington, VA, USA.Google Scholar
Thomas, A., Mastrogiovanni, F. and Baglietto, M. 2019. Task-Motion planning for navigation in belief space. In Proceedings of the 2019 International Symposium on Robotics Research (ISER). Hanoi, Vietnam.Google Scholar
Wakamatsu, H., Arai, E. and Hirai, S. 2006. Knotting/unknotting manipulation of deformable linear objects. International Journal of Robotic Research 25, 4, 371–395.CrossRefGoogle Scholar
Yamakawa, Y., Namiki, A. and Ishikawa, M. 2013. Dynamic high-speed knotting of a rope by a manipulator. International Journal of Advanced Robotic Systems 10, 1–12.CrossRefGoogle Scholar
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