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. The chapter begins with the formulation of the problem of learning trajectories in a PbD setting and presents the brief overview of statistical methods for task representation. The task modeling and task analysis phases encode the recorded data into compact and flexible representation of demonstrated motions and extract the relevant task features for achieving the required robot performance. Statistical methods have been widely used in robotics for representing the uncertain information about the state of the environment. The statistical algorithms provide a form to en capsulate the random variations in the observed demonstrations by deriving the probability distributions of the outcomes from several repeated measurements.
Task Representation
10.03.2017
8 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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