Underactuated legged robots depict highly nonlinear and complex dynamical behaviors that create significant challenges in accurately modeling system dynamics using both first principles and system identification approaches. Hence, the design of stabilizing controllers becomes more challenging due to inaccurate modeling. Suppose physical parameters on mathematical models have miscalibrations due to uncertainty in identifying and modeling processes. In that case, designed controllers could perform poorly or even result in unstable responses. Moreover, these parameters can change over time due to operation and environmental conditions. In that respect, analogous to a living organism modifying its behavior in response to novel conditions, adapting/updating system parameters, such as spring constant to compensate for modeling errors, could provide the advantage of constructing a stable gait level controller without needing "exact" dynamical parameter values. This paper presents an online, model-based adaptive control approach for an underactuated planar hexapod robot's pronking behavior adopted from antelope species. We show through systematic simulation studies that the adaptive control policy is robust to high levels of parameter uncertainties compared to a non-adaptive model-based dead-beat controller.