The Robotics Institute

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 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.

Notes

Associated Project(s): Dynamic Biped

Number of pages: 13

Text Reference

Jun Morimoto, Jun Nakanishi, Gen Endo, Gordon Cheng, Chris Atkeson, and Garth Zeglin, "Acquisition of a Biped Walking Pattern Using an Approximate Poincare Map," Proceedings of the IEEE-RAS/RSJ Int. Conf. on Humanoid Robots (Humanoids 2004), November, 2004, pp. 912 - 924.

BibTeX Reference

@inproceedings{Morimoto_2004_5594,
   author = "Jun Morimoto and Jun Nakanishi and Gen Endo and Gordon Cheng and Chris Atkeson and Garth Zeglin",
   title = "Acquisition of a Biped Walking Pattern Using an Approximate Poincare Map",
   booktitle = "Proceedings of the IEEE-RAS/RSJ Int. Conf. on Humanoid Robots (Humanoids 2004)",
   pages = "912 - 924",
   month = "November",
   year = "2004",
}

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