  Robust and energy-efficient bipedal locomotion in robotics is still a
challenging topic. In order to address issues in this field, we can take
inspiration from nature, by studying human locomotion. The Spring-Loaded
Inverted Pendulum (SLIP) model has shown to be a good model for this purpose.
However, the human musculoskeletal system enables us to actively modulate leg
stiffness, for example when walking in rough terrain with irregular and
unexpected height variations of the walking surface. This ability of varying
leg stiffness is not considered in conventional SLIP-based models, and
therefore this paper explores the potential role of active leg stiffness
variation in bipedal locomotion. It is shown that the conceptual SLIP model can
be iteratively extended to more closely resemble a realistic (i.e., non-ideal)
walker, and that feedback control strategies can be designed that reproduce the
SLIP behavior in these extended models. We show that these extended models
realize a cost of transport comparable to human walking, which indicates that
active leg stiffness variation plays an important role in human locomotion that
was previously not captured by the SLIP model. The results of this study show
that active leg stiffness adaptation is a promising approach for realizing more
energy-efficient and robust bipedal walking robots.
