Abstract

Movement prioritization is a common approach
to combine controllers of different tasks for redundant robots,
where each task is assigned a priority. The priorities of the
tasks are often hand-tuned or the result of an optimization,
but seldomly learned from data. This paper combines Bayesian
task prioritization with probabilistic movement primitives to
prioritize full motion sequences that are learned from demonstrations.
Probabilistic movement primitives (ProMPs) can
encode distributions of movements over full motion sequences
and provide control laws to exactly follow these distributions.
The probabilistic formulation allows for a natural application of
Bayesian task prioritization. We extend the ProMP controllers
with an additional feedback component that accounts inaccuracies
in following the distribution and allows for a more
robust prioritization of primitives. We demonstrate how the
task priorities can be obtained from imitation learning and
how different primitives can be combined to solve even unseen
task-combinations. Due to the prioritization, our approach can
efficiently learn a combination of tasks without requiring individual
models per task combination. Further, our approach can
adapt an existing primitive library by prioritizing additional
controllers, for example, for implementing obstacle avoidance.
Hence, the need of retraining the whole library is avoided in
many cases. We evaluate our approach on reaching movements
under constraints with redundant simulated planar robots and
two physical robot platforms, the humanoid robot “iCub” and
a KUKA LWR robot arm.