On Google Scholar there are a lot of papers which explain the advantages of motion primitive: instead of searching inside the state-space (configuration space of a robot) the solver has to search inside the plan space of motion primitives. Sometimes this concept is called lattice graph.
Even if all papers are convinced about motion primitive in general, there is room for speculation about how exactly this idea should be implemented. Two different school of thought are out there:
- Machine Learning for generate motion primitive. This is based on q-learning, neural networks and motion capture. The projekt "poeticon" (Yiannis Aloimonos) is a good example.
- Handcoded motion primtive. This concept is based an manual coded Finite States-Machines (FSM) which can only solve a concrete example like "pushing the box". Additional functionality has to be implemented by hand.
The question is, which concept is better on real life examples?