Multi-expert learning of adaptive legged locomotion

Understand

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.