Gait emergence and adaptation in animals is unmatched in robotic systems. Animals can create and recover locomotive functions "on-the-fly" after an injury whereas locomotion controllers for robots lack robustness to morphological changes. In this work, we extend previous research on emergent interlimb coordination of legged robots based on coupled phase oscillators with force feedback terms. We investigate how the coupling weights between these phase oscillators can be extracted from the morphology with a fast and computationally lightweight method based on a combination of twitching and Hebbian learning to form sensor-motor maps. The coefficients of these maps create naturally scaled weights, which not only lead to robust gait limit cycles, but can also adapt to morphological modifications such as sensor loss and limb injuries within a few gait cycles. We demonstrate the approach on a robotic quadruped and hexapod.