Using efference copy and a forward internal model for adaptive biped walking
- 672 Downloads
To behave properly in an unknown environment, animals or robots must distinguish external from self-generated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its upper body component controller. As a consequence, the controller drives the upper body component (UBC) to lean forwards/backwards as soon as an error occurs resulting in dynamical stable walking. We have evaluated the performance of the system on four different track configurations. Furthermore we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion control to the benefit of modern neural control strategies in robots.
KeywordsEfference copy Forward internal model Neural network Biped robot Dynamic walking Walking machine
Online Resource (MPG 15.7 MB)
- Dürr, V., Krause, A., Schmitz, J., & Cruse, H. (2003). Neuroethological concepts and their transfer to walking machines. International Journal of Robotics Research, 22, 151–167. Google Scholar
- Held, R. (1961). Exposure history as a factor in maintaining stability of perception and coordination. Journal of Nervous and Mental Disease, 132, 26–32. Google Scholar
- Huang, W., Chew, C.-M., Zheng, Y., & Hong, G.-S. (2008). Pattern generation for bipedal walking on slopes and stairs. In 8th IEEE-RAS international conference on humanoids (pp. 205–210). Google Scholar
- Iida, S., Kondo, T., & Ito, K. (2006). An environmental adaptation mechanism for a biped walking robot control based on elicitation of sensorimotor constraints. SAB 2006. LNAI 4095 (pp. 174–184). Google Scholar
- Iida, F., & Tedrake, R. (2009). Minimalistic control of a compass gait robot in rough terrain. In Proceedings of the IEEE/RAS international conference on robotics and automation (ICRA). IEEE/RAS (pp. 1985–1990). Google Scholar
- Manoonpong, P., Geng, T., & Wörgötter, F. (2006). Exploring the dynamic walking range of the biped robot “Runbot” with an active upper-body component. In Proceedings of the sixth IEEE-RAS international conference on humanoid robots (humanoids 2006) (pp. 418–424). Google Scholar
- Miyakoshi, S. (2006). Bipedal walking with a memory-based motion controller. Journal of the Robotics Society of Japan, 24(5), 623–631 (in Japanese). Google Scholar
- Namiki, A., Hashimoto, K., & Ishikawa, M. (2003). A hierarchical control architecture for high-speed visual servoing. International Journal of Robotics Research, 22(10–11), 873–888. Google Scholar
- Nissen, S. (2003). Implementation of a fast artificial neural network library (fann). Report: Department of Computer Science, University of Copenhagen (DIKU). Software available at http://www.sourceforge.net/projects/fann.
- Ogino, M., Toyama, H., Fuke, S., Mayer, N. M., Watanabe, A., & Asada, M. (2008). Compliance control for biped walking on rough terrain. In RoboCup 2007, LNAI 5001 (pp. 556–563). Google Scholar
- Russo, P., Webb, B., Reeve, R., Arena, P., & Patané, L. (2005). A cricket-inspired neural network for feedforward compensation and multisensory integration. In Proceedings of the 44th IEEE conference on decision and control and European control conference, Sevilla, Spain (pp. 227–232). Google Scholar
- Schröder-Schetelig, J., Manoonpong, P., & Wörgötter, F. (2008). Using efference copy and neural control for adaptive walking on different terrains. In Frontiers in computational neuroscience. Conference abstract: Bernstein symposium 2008. doi: 10.3389/conf.neuro.10.2008.01.115.
Open AccessThis is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.