PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Trajectory generation for dynamic bipedal walking through qualitative model based manifold learning
Subramanian Ramamoorthy and Benjamin Kuipers
In: IEEE International Conference on Robotics and Automation, 2008., 19-23 May 2008, Pasadena, USA.

Abstract

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4828
Deposited By:Subramanian Ramamoorthy
Deposited On:24 March 2009