Our goal is to introduce a more appropriate method of representing, generalising and comparing gaits; particularly, walking gait. Human walking gaits are a result of complex, interdependent factors that include variations resulting from embodiments, environment and tasks, making techniques that use average template frameworks suboptimal for systematic analysis or corrective interventions. The proposed work aims to devise methodologies for being able to represent gaits and gait transitions such that optimal policies that eliminate the inter-personal variations from tasks and embodiments may be recovered. Our approach is built upon (i) work in the domain of nullspace policy recovery and (ii) previous work in generalisation for point-to-point movements. The problem is formalised using a walking-phase model, and the nullspace learning method is used to generalise a consistent policy from multiple observations with rich variations. Once recovered, the underlying policies (mapped to different gait phases) can serve as reference guideline to quantify and identify pathological gaits while being robust against interpersonal and task variations. To validate our methods, we have demonstrated robustness of our method with simulated sagittal two-link gait data with multiple ground truth constraints and policies. Pathological gait identification was then tested on real-world human gait data with induced gait abnormality, with the proposed method showing significant robustness to variations in speed and embodiment compared to template-based methods. Future work will extend this to kinetic features and higher dimensional features.