Skip to main content
Log in

Simulating Robots Without Conventional Physics: A Neural Network Approach

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

The construction of physics-based simulators for use in Evolutionary Robotics (ER) can be complex and time-consuming. Alternative simulation schemes construct robotic simulators from empirically-collected data. Such empirical simulators, however, also have associated challenges. This paper therefore investigates the potential use of Artificial Neural Networks, henceforth simply referred to as Neural Networks (NNs), as alternative robotic simulators. In contrast to physics models, NN-based simulators can be constructed without requiring an explicit mathematical model of the system being modeled, which can simplify simulator development. The generalization abilities of NNs, along with NNs’ noise tolerance, suggest that NNs could be well-suited to application in robotics simulation. Investigating whether NNs can be effectively used as robotic simulators in ER is thus the endeavour of this work. Two robot morphologies were selected on which the NN simulators created in this work were based, namely a differentially steered robot and an inverted pendulum robot. Accuracy tests indicated that NN simulators created for these robots generally trained well and could generalize well on data not presented during simulator construction. In order to validate the feasibility of the created NN simulators in the ER process, these simulators were subsequently used to evolve controllers in simulation, similar to controllers developed in related studies. Encouraging results were obtained, with the newly-evolved controllers allowing experimental robots to exhibit obstacle avoidance, light-approaching behaviour and inverted pendulum stabilization. It was thus clearly established that NN-based robotic simulators can be successfully employed as alternative simulation schemes in the ER process.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Anderson, C.: Learning to control an inverted pendulum using Neural Networks. IEEE Control Syst. Mag. 9, 31–37 (1989)

    Article  Google Scholar 

  2. Baldassarre, G., Nolfi, S., Parisi, D.: Evolving mobile robots able to display collective behaviors. Artif. Life 9, 255–267 (2003)

    Article  Google Scholar 

  3. Basheer, I.A., Hajmeer, M.: Artificial Neural Networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000)

    Article  Google Scholar 

  4. Brooks, R.A.: New approaches to robotics. Science 253(5025), 1227–1232 (1991)

    Article  Google Scholar 

  5. Brooks, R.A.: Artificial life and real robots. In: Proceedings of the First European Conference on Artifi cial Life, pp. 3–10. MIT Press, Cambridge (1992)

    Google Scholar 

  6. Carpin, S., Stoyanov, T., Nevatia, Y.: Quantitative assessments of USARSim accuracy. In: Proceedings of Performance Metrics for Intelligent Systems Workshop (2006)

  7. Chai, K.M.A., Williams, C.K.I., Klanke, S., Vijayakumar, S.: Multi-task Gaussian process learning of robot inverse dynamics. In: Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, pp. 265–272 (2008)

  8. Cohen, J.D., Lin, M.C., Manocha, D., Ponamgi, M.: I-COLLIDE: an interactive and exact collision detection system for large-scale environments. In: Proceedings of the 1995 Symposium on Interactive 3D graphics. ACM, New York (1995)

    Google Scholar 

  9. Colton, S.: The balance filter: a simple solution for integrating accelerometer and gyroscope measurements for a balancing platform. White paper, Massachusetts Institute of Technology (2007)

  10. Deb, K., Agrawal, R.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)

    MathSciNet  MATH  Google Scholar 

  11. Floreano, D., Husbands, P., Nolfi, S.: Evolutionary Robotics. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics. Springer, Berlin (2008)

    Google Scholar 

  12. Floreano, D., Mondada, F.: Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot. In: Proceedings of the Third International Conference on Simulation of Adaptive Behavior: From Animals to Animats 3, pp. 421–430. MIT Press, Cambridge (1994)

    Google Scholar 

  13. Gallant, S.I.: Neural Network Learning and Expert Systems. MIT Press, Cambridge (1993)

    MATH  Google Scholar 

  14. Grzeszczuk, R., Terzopoulos, D., Hinton, G.: Neuroanimator: fast neural network emulation and control of physics-based models. In: Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques, pp. 9–20. ACM, New York (1998)

    Google Scholar 

  15. Hartland, C., Bredeche, N.: Evolutionary robotics: from simulation to the real world using anticipation. In: ABIALS (2006). http://hal.inria.fr/inria-00120115/en/

  16. Harvey, I., Husbands, P., Cliff, D.: Issues in evolutionary robotics. In: Proceedings of the Second International Conference on Simulation of Adaptive Behavior: From Animals to Animats 2, pp. 364–373. MIT Press, Cambridge (1993)

    Google Scholar 

  17. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, Englewood Cliffs (2008)

    Google Scholar 

  18. Jacobi, N.: Running across the reality gap: octopod locomotion evolved in a minimal simulation. In: Proceedings of the First European Workshop on Evolutionary Robotics, pp. 39–58. Springer, London (1998)

    Chapter  Google Scholar 

  19. Jacobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: the use of simulation in evolutionary robotics. In: Proceedings of the Third European Conference on Advances in Artificial Life, pp. 704–720. Springer, London (1995)

    Chapter  Google Scholar 

  20. Kodjabachian, J., Meyer, J.: Evolution and development of neural controllers for locomotion, gradient-following, and obstacle-avoidance in artificial insects. IEEE Trans. Neural Netw. 9(5), 796–812 (1998)

    Article  Google Scholar 

  21. Koenig, N., Howard, A.: Design and use paradigms for Gazebo, an open-source multi-robot simulator. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2149–2154 (2004)

  22. Kyriacou, T., Nehmzow, U., Iglesias, R., Billings, S.A.: Accurate robot simulation through system identification. Robot. Auton. Syst. 56(12), 1082–1093 (2008)

    Article  Google Scholar 

  23. Laue, T., Spiess, K., Röfer, T.: Simrobot—a general physical robot simulator and its application in RoboCup. In: RoboCup 2005: Robot Soccer World Cup IX. Lecture Notes in Computer Science, pp. 173–183. Springer, Berlin (2006)

    Chapter  Google Scholar 

  24. Lee, T., Nehmzow, U., Hubbold, R.J.: Mobile robot simulation by means of acquired Neural Network models. In: Proceedings of the 12th European Simulation Multiconference on Simulation—Past, Present and Future, pp. 465–469 (1998)

  25. LEGO.com MINDSTORMS: Home. mindstorms.lego.com (2010). Accessed Jan 2010

  26. Li, S., Huo, C., Liu, Y.: Inverted pendulum system control by using modified PID Neural Network. In: Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control, p. 426. IEEE Computer Society, Washington, DC (2008)

    Chapter  Google Scholar 

  27. Lipson, H., Bongard, J.C., Zykov, V., Malone, E.: Evolutionary robotics for legged machines: from simulation to physical reality. In: Intelligent Autonomous Systems, pp. 11–18 (2006)

  28. Lund, H.H., Miglino, O.: From simulated to real robots. In: Proceedings of IEEE Third International Conference on Evolutionary Computation (1996)

  29. Meeden, L., Mcgraw, G., Blank, D.: Emergent control and planning in an autonomous vehicle. In: Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (1993)

  30. Meeden, L.A., Kumar, D.: Trends in evolutionary robotics. In: Jain, L., Fukuda, T. (eds.) Soft Computing for Intelligent Robotic Systems, pp. 215–233. Physica, New York (1998)

    Chapter  Google Scholar 

  31. Miglino, O., Nafasi, K., Taylor, C.E.: Selection for wandering behavior in a small robot. Artif. Life 2(1), 101–116 (1995)

    Article  Google Scholar 

  32. Miglino, O., Lund, H.H., Nolfi, S.: Evolving mobile robots in simulated and real environments. Artif. Life 2, 417–434 (1996)

    Article  Google Scholar 

  33. Miranda, J.L.C.: Application of Kalman filtering and PID control for direct inverted pendulum control. Master’s thesis, California State University (2009)

  34. Miyashita, S., Flurin, K., Lungarella, M., Pfeifer, R.: Cutting Edge Robotics. InTech, Rijeka (2009)

    Google Scholar 

  35. Moreno, P.O., Hernandez Ruiz, S.I., Valenzuela, J.C.R.: Simulation and animation of a 2 degree of freedom planar robot arm based on Neural Networks. In: Proceedings of the Electronics, Robotics and Automotive Mechanics Conference, pp. 488–493. IEEE Computer Society, Washington, DC (2007)

    Chapter  Google Scholar 

  36. Nawawi, S., Ahmad, M., Osman, J.: Control of two-wheels inverted pendulum mobile robot using full order sliding mode control. In: Proceedings of International Conference on Man-Machine Systems (2006)

  37. Nolfi, S., Parisi, D.: Evolving non-trivial behaviors on real robots: an autonomous robot that picks up objects. In: Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence, pp. 243–254. Springer, London (1995)

    Google Scholar 

  38. Omatu, S., Fujinaka, T., Yoshioka, M.: Neuro-PID control for inverted single and double pendulums. In: IEEE International Conference on Systems, Man, and Cybernetics (2000)

  39. Pasemann, F.: Evolving neurocontrollers for balancing an inverted pendulum. In: Network: Computation in Neural Systems, pp. 495–511 (1997)

  40. Pathak, K., Franch, J., Agrawal, S.K.: Velocity and position control of a wheeled inverted pendulum by partial feedback linearization. IEEE Trans. Robot. 21(3), 505–513 (2005)

    Article  Google Scholar 

  41. Pretorius, C.J.: Artificial Neural Networks as simulators for behavioural evolution in evolutionary robotics. Master’s thesis, Nelson Mandela Metropolitan University (2010)

  42. Pretorius, C.J., du Plessis, M.C., Cilliers, C.B.: Towards an artificial neural network-based simulator for behavioural evolution in evolutionary robotics. In: Proceedings of the 2009 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists, pp. 170–178. ACM, New York (2009)

    Google Scholar 

  43. Pretorius, C.J., du Plessis, M.C., Cilliers, C.B.: A Neural Network-based kinematic and light-perception simulator for simple robotic evolution. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)

  44. Rieffel, J., Saunders, F., Nadimpalli, S., Zhou, H., Hassoun, S., Rife, J., Trimmer, B.: Evolving soft robotic locomotion in PhysX. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, pp. 2499–2504. ACM, New York (2009)

    Chapter  Google Scholar 

  45. Sivanandam, S., Deepa, S.: Introduction to Genetic Algorithms. Springer, New York (2008)

    MATH  Google Scholar 

  46. Sofge, D.A., Potter, M.A., Bugajska, M.D., Schultz, A.C.: Challenges and opportunities of evolutionary robotics. In: Proceedings of the Second International Conference on Computational Intelligence, Robotics and Autonomous Systems (2003)

  47. Teo, J.: Robustness of artificially evolved robots: what’s beyond the evolutionary window? In: Proceedings of the Second International Conference on Artificial Intelligence in Engineering and Technology, pp. 14–20. Kota Kinabalu, Sabah, Malaysia (2004)

  48. Tikhanoff, V., Cangelosi, A., Fitzpatrick, P., Metta, G., Natale, L., Nori, F.: An open-source simulator for cognitive robotics research: the prototype of the iCub humanoid robot simulator. In: Performance Metrics for Intelligent Systems Workshop. National Institute of Standards and Technology (2008)

  49. Vadakkepat, P., Tan, K.C., Ming-Liang, W.: Evolutionary artificial potential fields and their application in real time robot path planning. In: Proceedings of the 2000 Congress on Evolutionary Computation, pp. 256–263 (2000)

  50. Van de Velde, W.: Toward learning robots. Robot. Auton. Syst. 8(1–2), 1–6 (1991)

    Article  Google Scholar 

  51. Watson, R.A., Ficici, S.G., Pollack, J.B.: Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robot. Auton. Syst. 39(1), 1–18 (2002)

    Article  Google Scholar 

  52. Zagal, J.C., Ruiz-Del-Solar, J.: Combining simulation and reality in evolutionary robotics. J. Intell. Robot. Syst. 50(1), 19–39 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. J. Pretorius.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pretorius, C.J., du Plessis, M.C. & Cilliers, C.B. Simulating Robots Without Conventional Physics: A Neural Network Approach. J Intell Robot Syst 71, 319–348 (2013). https://doi.org/10.1007/s10846-012-9782-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-012-9782-6

Keywords

Navigation