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Carnegie Mellon Robotics Institute

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Abstract

We propose a model-based reinforcement learning algorithm for biped walking in which the robot learns to appropriately modulate an observed walking pattern. Via-points are detected from the observed walking trajectories using the minimum jerk criterion. The learning algorithm modulates the via-points as control actions to improve walking trajectories. This decision is based on a learned model of the Poincare map of the periodic walking pattern. The model maps from a state in the single support phase and the control actions to a state in 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 policies are acquired.

Notes

Associated Project(s): Dynamic Biped

Number of pages: 6

Text Reference

Jun Morimoto, Jun Nakanishi, Gen Endo, Gordon Cheng, Chris Atkeson, and Garth Zeglin, "Poincare-Map-Based Reinforcement Learning for Biped Walking," Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'05), April, 2005.

BibTeX Reference

@inproceedings{Morimoto_2005_5593,
   author = "Jun Morimoto and Jun Nakanishi and Gen Endo and Gordon Cheng and Chris Atkeson and Garth Zeglin",
   title = "Poincare-Map-Based Reinforcement Learning for Biped Walking",
   booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'05)",
   month = "April",
   year = "2005",
}

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