Legged robots represent great promise for transport in unstructured environments. However, it has been difficult to devise motion planning strategies that achieve a combination of energy efficiency, safety and flexibility comparable to legged animals. In this paper, we address this issue by presenting a trajectory generation strategy for dynamic bipedal walking robots using a factored approach to motion planning - combining a low-dimensional plan (based on intermittently actuated passive walking in a compass-gait biped) with a manifold learning algorithm that solves the problem of embedding this plan in the high-dimensional phase space of the robot. This allows us to achieve task level control (over step length) in an energy efficient way - starting with only a coarse qualitative model of the system dynamics and performing a data-driven approximation of the dynamics in order to synthesize families of dynamically realizable trajectories. We demonstrate the utility of this approach with simulation results for a multi-link legged robot.

PDF, HTML

In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA-08) 2008.

@inproceedings{ramamoorthy:icra08, title={Trajectory generation for dynamic bipedal walking through qualitative model based manifold learning}, author={Subramanian Ramamoorthy and Benjamin Kuipers}, booktitle={Proceedings of the IEEE International Conference on Robotics and Automation (ICRA-08)}, url="http://www.cs.utexas.edu/users/ai-lab?ramamoorthy:icra08", year={2008} }