Simultaneous learning of hierarchy and primitives for complex robot tasks

Abstract

We present a new interaction paradigm for robot learning from demonstration, called simultaneous learning of hierarchy and primitives (SLHAP), in which information about hierarchy and primitives is naturally interleaved in a single, coherent demonstration session. A key innovation in the new paradigm is the human demonstrator’s narration of primitives as he executes them, which allows the system to identify the boundaries between primitives. Hierarchy is represented using hierarchical task networks; motion planning constraints on the primitives are represented using task space regions. We implemented SLHAP on an autonomous robot and produced an interaction video illustrating its effectiveness learning a complex task with five levels of hierarchy and eight types of primitives. The underlying algorithms which make SLHAP possible are described and evaluated.