This chapter reviews approaches for task representation in robot programming by demonstration (PbD). Based on the level of task abstraction, the methods are categorized into high‐level task representation at the symbolic level of abstraction and low‐level task representation at the trajectory level of abstraction. Techniques for data preprocessing related to trajectories scaling and aligning are also discussed in the chapter.
The PbD framework aims at learning from multiple demonstrations of a skill performed under similar conditions. For a set of M demonstrations, the perception data are denoted by , where m is used for indexing the demonstrations, t is used for indexing the measurements within each demonstration, and Tm denotes the total number of measurements of the demonstration sequence Xm. Each measurement represents a D‐dimensional vector . The form of the measurements depends on the data acquisition system(s) employed for perception of the demonstrations, and it can encompass the following: