Humans attribute meaning to actions, and can recognize, imitate, predict, compose from parts, and analyse complex actions performed by other humans. We have built a model of action representation and understanding which takes as input perceptual data of humans performing manipulatory actions and finds a semantic interpretation of it. It achieves this by representing actions as minimal plans based on a few primitives. The motivation for our approach is to have a description, that abstracts away the variations in the way humans perform actions. The model can be used to represent complex activities on the basis of simple actions. The primitives of these minimal plans are embodied in the physicality of the system doing the analysis. The model understands an action under observation by recognising which plan is occurring. Using primitives thus rooted in its own physical structure, the model has a semanticist and causal understanding of what it observes. Using plans, the model considers actions as well as complex activities in terms of causality, compositions, and goal achievement, enabling it to perform complex tasks like prediction of primitives, separation of interleaved actions and filtering of perceptual input. We use our model over an action dataset involving humans using hand tools on objects in a constrained universe to understand an activity it has not seen before in terms of actions whose plans it knows of. The model thus illustrates a novel approach of understanding human actions by a robot.