Bipedalism is a distinguishing feature of our species and, as such, there has been much interest in the energetic costs and foraging returns of walking and running, especially among hunter-gatherer societies. However, humans routinely exhibit extensive locomotor versatility, with hunter-gatherers consistently also swimming, diving, and climbing. Additionally, the fitness costs and benefits of locomotion extend well beyond energy income and expenditure. Here, we review evidence from over 900 ethnographic documents across a worldwide sample of more than 50 hunter-gatherer societies to examine the fitness costs and benefits of walking, running, climbing, swimming, and diving. We show that the fitness costs and benefits of locomotor engagement consistently extend well beyond energetics to include, for example, currencies of status, protection from hazards, and risks of injury or death. These fitness factors differ in significance between locomotor modalities, with implications for the comparison of bipedal and non-bipedal locomotion. For example, while energetic demands represent the major cost of most bipedal engagements, the fitness implications of potential fall injuries may outweigh those of energetics in tree climbing. These results inform existing debates relating to hominin locomotor evolution and hunter-gatherer behavioural ecology.

Human rhythmic movement is generated by central pattern generators (CPGs), and their application to robot control has attracted interest of many scientists. But the coupling relationship between the central nervous system and the CPG network with external inputs is still not unveiled. According to biological experiment results, the CPG network is controlled by the neural system; in other words, the interaction between the central nervous system and the CPG network can control human movement effectively. This paper offers a complex human locomotion model, which illustrates the coupling relationship between the central nervous system and the CPG network with proprioception. Based on Matsuoka's CPG model (K. Matsuoka, Biol. Cybern. 52(6), 367–376 (1985)), the stability and robustness of the CPG network are analyzed with external inputs. In order to simulate the coupling relationship, the Radial Basis Function (RBF) neural network is used to simulate the cerebral cortex, and the Credit-Assignment Cerebellar Model Articulation Controller algorithm is employed to realize the locomotion mode conversion. A seven-link biped robot is chosen to simulate the walking gait. The main discoveries include: (1) the output of a new CPG network, which is stable and robust, can be treated as proprioception. Proprioception provides the central nervous system with the information about all joint angles; (2) analysis on a new locomotion model reveals that the cerebral cortex can modulate CPG parameters, leading to adjustment in walking gait.