  Complex manipulation tasks can contain various execution branches of
primitive skills in sequence or in parallel under different scenarios. Manual
specifications of such branching conditions and associated skill parameters are
not only error-prone due to corner cases but also quickly untraceable given a
large number of objects and skills. On the other hand, learning from
demonstration has increasingly shown to be an intuitive and effective way to
program such skills for industrial robots. Parameterized skill representations
allow generalization over new scenarios, which however makes the planning
process much slower thus unsuitable for online applications. In this work, we
propose a hierarchical and compositional planning framework that learns a
Geometric Task Network (GTN) from exhaustive planners, without any manual
inputs. A GTN is a goal-dependent task graph that encapsulates both the
transition relations among skill representations and the geometric constraints
underlying these transitions. This framework has shown to improve dramatically
the offline learning efficiency, the online performance and the transparency of
decision process, by leveraging the task-parameterized models. We demonstrate
the approach on a 7-DoF robot arm both in simulation and on hardware solving
various manipulation tasks.

Robotics