Achieving versatile robot locomotion requires motor skills which can adapt to
previously unseen situations. We propose a Multi-Expert Learning Architecture
(MELA) that learns to generate adaptive skills from a group of representative
expert skills. During training, MELA is first initialised by a distinct set of
pre-trained experts, each in a separate deep neural network (DNN). Then by
learning the combination of these DNNs using a Gating Neural Network (GNN),
MELA can acquire more specialised experts and transitional skills across
various locomotion modes. During runtime, MELA constantly blends multiple DNNs
and dynamically synthesises a new DNN to produce adaptive behaviours in
response to changing situations. This approach leverages the advantages of
trained expert skills and the fast online synthesis of adaptive policies to
generate responsive motor skills during the changing tasks. Using a unified
MELA framework, we demonstrated successful multi-skill locomotion on a real
quadruped robot that performed coherent trotting, steering, and fall recovery
autonomously, and showed the merit of multi-expert learning generating
behaviours which can adapt to unseen scenarios
