Skip to main content

Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions


Obstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.

Availability of Data and Materials

The presented framework is publicly available at


  1. Albrecht, S., Ramirez-Amaro, K., Ruiz-Ugalde, F., Weikersdorfer, D., Leibold, M., Ulbrich, M., Beetz, M.: Imitating human reaching motions using physically inspired optimization principles. In: 2011 11th IEEE-RAS International Conference on Humanoid Robots, pp 602–607. IEEE (2011)

  2. Beeson, P., Ames, B.: Trac-Ik: An open-source library for improved solving of generic inverse kinematics. In: 2015 IEEE-RAS 15Th International Conference on Humanoid Robots (Humanoids), pp 928–935. IEEE (2015)

  3. Duan, J., Ou, Y., Hu, J., Wang, Z., Jin, S., Xu, C.: Fast and stable learning of dynamical systems based on extreme learning machine. IEEE Trans Syst Man Cybern. Syst. (99) 1–11 (2017)

  4. Fahimi, F., Nataraj, C., Ashrafiuon, H.: Real-time obstacle avoidance for multiple mobile robots. Robotica 27(2), 189 (2009)

    Article  Google Scholar 

  5. Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998)

    Article  Google Scholar 

  6. Gams, A., Nemec, B., Ijspeert, A.J., Ude, A.: Coupling movement primitives: Interaction with the environment and bimanual tasks. IEEE Trans. Robot. 30(4), 816–830 (2014)

    Article  Google Scholar 

  7. Gasparetto, A., Zanotto, V.: A new method for smooth trajectory planning of robot manipulators. Mechan. Machine Theory 42(4), 455–471 (2007)

    MathSciNet  Article  Google Scholar 

  8. Ginesi, M., Meli, D., Calanca, A., Dall’Alba, D., Sansonetto, N., Fiorini, P.: Dynamic movement primitives: Volumetric obstacle avoidance. In: 2019 19th International Conference on Advanced Robotics (ICAR), pp 234–239 (2019),

  9. Ginesi, M., Sansonetto, N., Fiorini, P.: Overcoming some drawbacks of dynamic movement primitives. arXiv:1908.10608 (2019)

  10. Hoffmann, H., Pastor, P., Park, D.H., Schaal, S.: Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance. In: Robotics and Automation, 2009. ICRA’09. IEEE International Conference On, pp 2587–2592. IEEE (2009)

  11. Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1-3), 489–501 (2006)

    Article  Google Scholar 

  12. Huang, R., Cheng, H., Guo, H., Chen, Q., Lin, X.: Hierarchical Interactive Learning for a Human-Powered Augmentation Lower Exoskeleton. In: Robotics and Automation (ICRA), 2016 IEEE International Conference On, pp 257–263. IEEE (2016)

  13. Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: Learning attractor models for motor behaviors. Neural computation 25(2), 328–373 (2013)

    MathSciNet  Article  Google Scholar 

  14. Ijspeert, A.J., Nakanishi, J., Schaal, S.: Movement imitation with nonlinear dynamical systems in humanoid robots. In: Robotics and Automation, 2002. Proceedings. ICRA’02. IEEE International Conference On, vol. 2, pp 1398–1403. IEEE (2002)

  15. Ijspeert, A.J., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. In: Advances in Neural Information Processing Systems, pp 1547–1554 (2003)

  16. Joshi, R.P., Koganti, N., Shibata, T.: Robotic cloth manipulation for clothing assistance task using dynamic movement primitives. In: Proceedings of the Advances in Robotics, p 14. ACM (2017)

  17. Khansari-Zadeh, S.M., Billard, A.: Learning stable nonlinear dynamical systems with gaussian mixture models. IEEE Trans. Robot. 27(5), 943–957 (2011)

    Article  Google Scholar 

  18. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings 1985 IEEE International Conference on Robotics and Automation, vol. 2, pp 500–505. IEEE (1985)

  19. Khosla, P., Volpe, R.: Superquadric artificial potentials for obstacle avoidance and approach. In: Proceedings. 1988 IEEE International Conference on Robotics and Automation, pp 1778–1784. IEEE (1988)

  20. Lin, C., Chang, P., Luh, J.: Formulation and optimization of cubic polynomial joint trajectories for industrial robots. IEEE Trans. Autom. Control 28(12), 1066–1074 (1983)

    Article  Google Scholar 

  21. Magid, E., Keren, D., Rivlin, E., Yavneh, I.: Spline-based robot navigation. In: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference On, pp 2296–2301. IEEE (2006)

  22. Matsubara, T., Hyon, S.H., Morimoto, J.: Learning stylistic dynamic movement primitives from multiple demonstrations. In: Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference On, pp 1277–1283. Citeseer (2010)

  23. Park, D.H., Hoffmann, H., Pastor, P., Schaal, S.: Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields. In: Humanoid Robots, 2008. Humanoids 2008. 8th IEEE-RAS International Conference On, pp 91–98. IEEE (2008)

  24. Pastor, P., Hoffmann, H., Asfour, T., Schaal, S.: Learning and generalization of motor skills by learning from demonstration. In: Robotics and Automation, 2009. ICRA’09. IEEE International Conference On, pp 763–768. IEEE (2009)

  25. Pastor, P., Kalakrishnan, M., Righetti, L., Schaal, S.: Towards associative skill memories. In: Humanoid Robots (Humanoids), 2012 12th IEEE-RAS International Conference On, pp 309–315. IEEE (2012)

  26. Pastor, P., Righetti, L., Kalakrishnan, M., Schaal, S.: Online movement adaptation based on previous sensor experiences. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 365–371 (2011)

  27. Perdereau, V., Passi, C., Drouin, M.: Real-time control of redundant robotic manipulators for mobile obstacle avoidance. Robot. Auton. Syst. 41(1), 41–59 (2002)

    Article  Google Scholar 

  28. Rai, A., Meier, F., Ijspeert, A., Schaal, S.: Learning coupling terms for obstacle avoidance. In: 2014 IEEE-RAS International Conference on Humanoid Robots, pp 512–518. IEEE (2014)

  29. Rai, A., Sutanto, G., Schaal, S., Meier, F.: Learning feedback terms for reactive planning and control. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp 2184–2191. IEEE (2017)

  30. Ratliff, N., Zucker, M., Bagnell, J.A., Srinivasa, S.: Chomp: Gradient optimization techniques for efficient motion planning. In: Robotics and Automation, 2009. ICRA’09. IEEE International Conference On, pp 489–494. IEEE (2009)

  31. Rezaee, H., Abdollahi, F.: Adaptive artificial potential field approach for obstacle avoidance of unmanned aircrafts. In: 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp 1–6. IEEE (2012)

  32. Rimon, E., Koditschek, D.E.: Exact robot navigation using artificial potential functions. IEEE Trans. Robot. Autom. 8(5), 501–518 (1992)

    Article  Google Scholar 

  33. Roberti, A., Piccinelli, N., Meli, D., Fiorini, P.: Rigid 3d calibration in a robotic surgery scenario. Hamlyn Symposium on Medical Robotics (HSMR) in submission (2020)

  34. Rohmer, E., Singh, S.P.N., Freese, M.: Coppeliasim (Formerly V-Rep): A versatile and scalable robot simulation framework. In: Proc. of The International Conference on Intelligent Robots and Systems (IROS) (2013)

  35. Saveriano, M., Franzel, F., Lee, D.: Merging position and orientation motion primitives. In: International Conference on Robotics and Automation (ICRA), 2019 (2019)

  36. Schaal, S.: Dynamic movement primitives-a framework for motor control in humans and humanoid robotics. In: Adaptive Motion of Animals and Machines, pp 261–280. Springer (2006)

  37. Sutanto, G., Su, Z., Schaal, S., Meier, F.: Learning sensor feedback models from demonstrations via phase-modulated neural networks. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp 1142–1149. IEEE (2018)

  38. Ude, A., Gams, A., Asfour, T., Morimoto, J.: Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE Trans. Robot. 26(5), 800–815 (2010)

    Article  Google Scholar 

  39. Ude, A., Nemec, B., Petrić, T., Morimoto, J.: Orientation in cartesian space dynamic movement primitives. In: Robotics and Automation (ICRA), 2014 IEEE International Conference On, pp 2997–3004. IEEE (2014)

  40. Volpe, R.: Real and artificial forces in the control of manipulators: theory and experiments. Ph.D. thesis, PhD thesis, Carnegie Mellon University Department of Physics (1990)

  41. Volpe, R., Khosla, P.: Manipulator control with superquadric artificial potential functions: Theory and experiments. IEEE Trans Syst Man Cybern 20(6), 1423–1436 (1990)

    Article  Google Scholar 

  42. Wang, R., Wu, Y., Chan, W.L., Tee, K.P.: Dynamic movement primitives plus: For enhanced reproduction quality and efficient trajectory modification using truncated kernels and local biases. In: Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference On, pp 3765–3771. IEEE (2016)

  43. Yan, Z., Jouandeau, N., Cherif, A.A.: A survey and analysis of multi-robot coordination. Int. J. Adv. Robot. Syst. 10(12), 399 (2013)

    Article  Google Scholar 

  44. Zhang, W., Rodríguez-seda, E.J., Deka, S.A., Amrouche, M., Hou, D., Stipanović, D.M., Leitmann, G.: Avoidance control with relative velocity information for lagrangian dynamics. J. Intell. Robot. Syst. 1–16 (2019)

Download references


Open access funding provided by Università degli Studi di Verona within the CRUI-CARE Agreement. This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, ARS (Autonomous Robotic Surgery) project, grant agreement No. 742671.

Author information

Authors and Affiliations



Conceptualization: Michele Ginesi. Data curation: Daniele Meli, Andrea Roberti. Formal Analysis: Michele Ginesi, Daniele Meli, Andrea Robeti. Funding acquisition: Paolo Fiorini. Investigation: Michele Ginesi, Daniele Meli, Andrea Roberti, Nicola Sansonetto. Methodology: Michele Ginesi, Daniele Meli, Andrea Roberti, Nicola Sansonetto. Project administration: Paolo Fiorini. Resources: Paolo Fiorini. Software: Michele Ginesi. Supervision: Nicola Sansonetto, Paolo Fiorini. Validation: Daniele Meli, Andrea Roberti. Visualization: Michele Ginesi, Daniele Meli, Andrea Roberti. Writing – original draft: Michele Ginesi, Daniele Meli. Writing – review and editing: Michele Ginesi, Daniele Meli, Nicola Sansonetto, Paolo Fiorini.

Corresponding author

Correspondence to Michele Ginesi.

Ethics declarations

Conflict of Interests

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ginesi, M., Meli, D., Roberti, A. et al. Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions. J Intell Robot Syst 101, 79 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Obstacle avoidance
  • Dynamic movement primitives
  • Learning from demonstration