An experimental iterative learning strategy for a biped performing semi-active walking
In this paper, an iterative learning strategy is proposed for a biped which performs semi-active walking in the way that during the single support phase of walking, only the joints in the support leg are actuated and those in the swing leg are unactuated. This strategy is intended to provide further experimental tuning on the actuated joint trajectories computed by a model-based optimization procedure. The strategy demands the biped to swing its leg repeatedly, and during each swinging the hip trajectory is iteratively modified by a learning law to minimize foot scuffing of the swing leg and yet keep the associated foot clearance to within a small limit for reducing power consumption. Experiments show that the learning strategy leads to a convergent hip trajectory after 11 iterations. Besides, when the trajectory is adopted for actual walking, the biped demonstrates a good balance between power efficiency and robustness to ground conditions.