Acquisition of a biped walking pattern using a Poincare map
- 28 July 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 912-924 Vol. 2
- https://doi.org/10.1109/ichr.2004.1442694
Abstract
We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately place the swing leg. This decision is based on a learned model of the Poincare map of the periodic walking pattern. The model maps from a state at a single support phase and foot placement to a state at the next single support phase. We applied this approach to both a simulated robot model and an actual biped robot. We show that successful walking patterns are acquired.Keywords
This publication has 15 references indexed in Scilit:
- Learning from demonstration and adaptation of biped locomotionRobotics and Autonomous Systems, 2004
- Locomotion control of a biped locomotion robot using nonlinear oscillatorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Dynamics filter - concept and implementation of online motion generator for human figuresPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Passive bipedal walking with phasic muscle contractionBiological Cybernetics, 1999
- Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learningArtificial Intelligence, 1999
- Constructive Incremental Learning from Only Local InformationNeural Computation, 1998
- A theory for cursive handwriting based on the minimization principleBiological Cybernetics, 1995
- Development of a Biped Walking Robot Compensating for Three-Axis Moment by Trunk Motion.Journal of the Robotics Society of Japan, 1993
- Biped LocomotionPublished by Springer Nature ,1990
- Legged Robots That BalanceIEEE Expert, 1986