Realistic Internal Dynamics Are Essential for Human-Like Control: An Optimal Feedback Control Perspective

Humans skillfully control objects with internal dynamics, such as a bag of groceries that swings or a cup of coffee; yet the neural control principles underlying such dexterous coordination are not fully understood. An important question that remains to be answered is: How complex are humans’ internalized representations of such systems (also known as the internal models)? This question has been tackled before using various model-based control architectures; however, the answers within the context of the leading neural control theory—the stochastic optimal feedback control (OFC)—remain elusive. To shed more light on this question, we ran OFC simulations of transporting an underactuated cart-pendulum system with varying levels of internal model detail and compared the results with human experimental data of the same task. Using OFC as the controller, our results showed that the internal model that matched the full dynamics of the cart-and-pendulum system reproduced human data most closely. These results are in contrast to a previous study that used input shaping as the control structure and concluded that a simplified internal model led to the most human-like behavior. In particular, when our internal model lacked impedance or coupling, the characteristic double-peak velocity profile did not emerge in simulation. But the full-detail internal model reproduced the characteristic two-peak velocity profile and maintained peak ratios consistent with experimental data, unlike simplified internal models which produced substantially larger ratios.. These results indicate that realistic internal dynamics and feedback structure are essential for capturing human-like manipulation, providing a blueprint for control policies and training of dexterous robots.