Practical bipedal walking control on uneven terrain using surface learning and push recovery

Abstract

Abstract-Bipedal walking in human environments is made difficult by the unevenness of the terrain and by external disturbances. Most approaches to bipedal walking in such environments either rely upon a precise model of the surface or special hardware designed for uneven terrain. In this paper, we present an alternative approach to stabilize the walking of an inexpensive, commercially-available, position-controlled humanoid robot in difficult environments. We use electrically compliant swing foot dynamics and onboard sensors to estimate the inclination of the local surface, and use a online learning algorithm to learn an adaptive surface model. Perturbations due to external disturbances or model errors are rejected by a hierarchical push recovery controller, which modulates three biomechanically motivated push recovery controllers according to the current estimated state. We use a physically realistic simulation with an articulated robot model and reinforcement learning algorithm to train the push recovery controller, and implement the learned controller on a commercial DARwIn-OP small humanoid robot. Experimental results show that this combined approach enables the robot to walk over unknown, uneven surfaces without falling down.

Active balancing and turning for alpine skiing robots

“…slopes, stepping fields) the gait can become unstable (Chen & Byl, 2012). The use of phase modification based on gyroscope data has been shown to mitigate this problem and allow the robot to walk across small changes in elevation without falling over (Baltes & Lam, 2004;McGrath et al, 2004;Adiwahono et al, 2010;Yi et al, 2011). (Sugihara et al, 2002).…”

Team THOR's Entry in the DARPA Robotics Challenge Trials 2013

“…The reactive walk controller (Yi et al., ) is based on the analytic solution of the linear inverted pendulum model (LIPM) dynamics equation with fixed height. To get the solution in a closed form, we specified the reference ZMP trajectory as a piecewise linear function and put constraints on the boundary conditions of the COM.…”

Push recovery system and balancing of a biped robot on steadily increasing slope of an inclined plane

“…The combined approach enabled the robot to walk on an unknown uneven terrain. In addition to the above approaches, Yi et al (2011aYi et al ( , 2011b again proposed a method consisting of low level controllers and high level controller that detects the current robot state. Reinforcement learning was used to optimise the parameters of the controllers in order to maximise the stability of the robot over a broad range of external disturbances.…”