Abstract
This paper compares two approaches to physics based, balancing systems, for 3D biped characters that can react to dynamic environments. The first approach, based on the concept of proprioception, use a neuro-controller to define the position and orientation of the joints involved in the motion. The second approach use a self-adaptive Proportional Derivative (PD) controller along with a neural network. Both neural networks were trained using a Genetic Algorithm (GA). The study showed that both approaches were capable of achieving balance and the GA proved to work well as a search strategy for both the neuro-controller and the PD-controller. The results also showed that the neuro-controller performed better but the PD-controller was more flexible and capable to recover under external disturbances such as wind drag and momentary collisions with objects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Parent, R.: Interpolation and basic techniques. In: Computer Animation (2007). https://doi.org/10.1016/b978-155860579-4/50004-5
Sims, K.: Evolving virtual creatures (2005). https://doi.org/10.1145/192161.192167
Geijtenbeek, T., van de Panne, M., van der Stappen, A.F.: Flexible muscle-based locomotion for Bipedal creatures. ACM Trans. Graph. https://doi.org/10.1145/2508363.2508399
Galanter, P.: Computational aesthetic evaluation: past and future. In: McCormack, J., d’Inverno, M. (eds.) Computers and Creativity, pp. 255–293. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31727-9
Yin, K.K., Loken, K., van de Panne, M.: SIMBICON, simple biped locomotion control. ACM Trans. Graph. 26, 105 (2007)
Yokoi, K., Kanehiro, F., Hirukawa, H., Kaneko, K., Kajita, S.: The 3D linear inverted pendulum mode: a simple modeling for a biped walking pattern generation (2002). https://doi.org/10.1109/iros.2001.973365
Vukobratovic, M., Juricic, D.: Contribution to the synthesis of biped gait. IEEE Trans. Bio-med. Eng. (2008). https://doi.org/10.1109/tbme.1969.4502596
Hirai, K., Hirose, M., Haikawa, Y., Takenaka, T.: The development of Honda humanoid robot. In: Proceedings - IEEE International Conference on Robotics and Automation (1998). https://doi.org/10.1109/ROBOT.1998.677288
Ziegler, J.G., Nichols, N.B.: Optimum settings for automatic controllers. InTech (1995). https://doi.org/10.1115/1.2899060
Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. (2002). https://doi.org/10.1162/106365602320169811
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning (1989). https://doi.org/10.1007/BF01920603
Risi, S., Togelius, J.: Neuroevolution in games: state of the art and open challenges. IEEE Trans. Comput. Intell. AI Games (2015). https://doi.org/10.1109/TCIAIG.2015.2494596
Liapis, A., Yannakakis, G.N., Togelius, J.: Neuroevolutionary constrained optimization for content creation. In: 2011 IEEE Conference on Computational Intelligence and Games, CIG 2011 (2011). https://doi.org/10.1109/CIG.2011
Luo, L., Yin, H., Cai, W., Zhong, J., Lees, M.: Design and evaluation of a data-driven scenario generation framework for game-based training. IEEE Trans. Comput. Intell. AI Games (2017). https://doi.org/10.1109/TCIAIG.2016.2541168
Geijtenbeek, T., Pronost, N.: Interactive character animation using simulated physics. Comput. Graph. Forum (2012). https://doi.org/10.1111/j.1467-8659.2012.03189.x
Qiu, G.Y., Wu, S.H.: Self-adjusting locomotion on a partially broken-down quadrupedal biomorphic robot by evolutionary algorithms. In: 2012 IEEE International Conference on Robotics and Biomimetics, ROBIO 2012 - Conference Digest (2012). https://doi.org/10.1109/ROBIO.2012.6490942
Hodgins, J.K., Wooten, W.L., Brogan, D.C., O’Brien, J.F.: Animating human athletics (2005). https://doi.org/10.1145/218380.218414
Cheng, M.Y., Lin, C.S.: Genetic algorithm for control design of biped locomotion. J. Robot. Syst. (1997). https://doi.org/10.1002/(SICI)1097-4563(199705)14:5<365::AID-ROB3>3.0.CO;2-N
Ayhan, O., Erbatur, K.: Biped robot walk control via gravity compensation techniques. In: IECON Proceedings (Industrial Electronics Conference) (2004). https://doi.org/10.1109/IECON.2004.1433381
Kho, J.W., Lim, D.C., Kuc, T.Y.: Implementation of an intelligent controller for biped walking robot using genetic algorithm. In: 2006 IEEE International Symposium on Industrial Electronics, vol. 1, pp. 49–54 (2006)
Heinen, M.R., Osrio, F.S.: Applying genetic algorithms to control gait of simulated robots. In: Electronics, Robotics and Automotive Mechanics Conference, CERMA 2007 - Proceedings (2007). https://doi.org/10.1109/CERMA.2007.4367736
Shafii, N., Javadi, M.H.S., Kimiaghalam, B.: A truncated fourier series with genetic algorithm for the control of biped locomotion. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM (2009). https://doi.org/10.1109/AIM.2009.5229814
Tsai, Y.Y., Lin, W.C., Cheng, K.B., Lee, J., Lee, T.Y.: Real-time physics-based 3D biped character animation using an inverted pendulum model. IEEE Trans. Vis. Comput. Graph. (2010). https://doi.org/10.1109/TVCG.2009.76
Coros, S., Beaudoin, P., van de Panne, M.: Generalized biped walking control. ACM Trans. Graph. (2010). https://doi.org/10.1145/1778765.1781156
Hong, Y.D., Park, C.S., Kim, J.H.: Stable bipedal walking with a vertical center-of-mass motion by an evolutionary optimized central pattern generator. IEEE Trans. Industr. Electron. (2014). https://doi.org/10.1109/TIE.2013.2267691
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Carlsen, C.S., Palamas, G. (2019). Evolving Balancing Controllers for Biped Characters in Games. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_72
Download citation
DOI: https://doi.org/10.1007/978-3-030-20518-8_72
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20517-1
Online ISBN: 978-3-030-20518-8
eBook Packages: Computer ScienceComputer Science (R0)