Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing

Learning and reproducing complex movements is an important skill for robots. However, while humans can learn and generalise new complex trajectories, robots are often programmed to execute point-by-point precise but fixed patterns. This study proposes a method for decomposing new complex trajectories into a set of known robot-based primitives. Instead of reproducing accurately an observed trajectory, the robot interprets it as a composition of its own previously acquired primitive movements. The method attempts initially a rough approximation with the idea of capturing the most essential features of the movement. Observing the discrepancy between the demonstrated and reproduced trajectories, the process then proceeds with incremental decompositions. The method is tested on both geometric and human generated trajectories. The shift from a data-centred view to an agent-centred view in learning trajectories results in generalisation properties like the abstraction to primitives and noise suppression. This study suggests a novel approach to learning complex robot motor patterns that builds upon existing motor skills. Applications include drawing, writing, movement generation and object manipulation in a variety of tasks.