The locomotion and balance capabilities of bipedal robots have greatly improved in recent years. However, maintaining balance on difficult terrain still poses a significant challenge. In this paper, we examined how humans maintain mediolateral balance when standing on a narrow beam with bare feet and wearing rigid soles. Our results show that foot-beam interaction dynamics critically influence balancing behavior. Importantly, this suggests that differences in human balancing behavior across different support surfaces may not solely result from changes in their neural control strategy. They may also result from changes in foot-ground interaction. Thus, the altered foot-ground interaction dynamics must be considered to accurately capture changes in the human controller across different support surfaces. A simplified model of foot-beam interaction was added to a double inverted pendulum model for human balancing. This extended model could replicate the change in human behavior across different foot contact conditions (bare feet vs. rigid feet). A better understanding of how humans coordinate whole-body behavior across a range of conditions may inform the development of balance controllers for bipedal robots.

An important issue in motor control is understanding the basic principles underlying the accomplishment of natural movements. According to optimal control theory, the problem can be stated in these terms: what cost function do we optimize to coordinate the many more degrees of freedom than necessary to fulfill a specific motor goal? This question has not received a final answer yet, since what is optimized partly depends on the requirements of the task. Many cost functions were proposed in the past, and most of them were found to be in agreement with experimental data. Therefore, the actual principles on which the brain relies to achieve a certain motor behavior are still unclear. Existing results might suggest that movements are not the results of the minimization of single but rather of composite cost functions. In order to better clarify this last point, we consider an innovative experimental paradigm characterized by arm reaching with target redundancy. Within this framework, we make use of an inverse optimal control technique to automatically infer the (combination of) optimality criteria that best fit the experimental data. Results show that the subjects exhibited a consistent behavior during each experimental condition, even though the target point was not prescribed in advance. Inverse and direct optimal control together reveal that the average arm trajectories were best replicated when optimizing the combination of two cost functions, nominally a mix between the absolute work of torques and the integrated squared joint acceleration. Our results thus support the cost combination hypothesis and demonstrate that the recorded movements were closely linked to the combination of two complementary functions related to mechanical energy expenditure and joint-level smoothness.