Morphology generalizable reinforcement learning via multi-level graph features

Controlling a group of robots with diverse morphologies using a unified policy, known as morphology generalizable control, is a challenging problem in robotic control. Existing graph neural network-based (GNN-based) methods suffer from inefficient modular communication due to non-adjacent modules having to communicate across multiple hops, while transformer-based methods neglect morphology prior information which is crucial for morphology generalizable control. To overcome these limitations, in this work, we propose MG2(Morphology Generalizable Reinforcement Learning via Multi-level Graph Features) which incorporates multi-level graph features derived from the morphology graph into the transformer architecture. To effectively incorporate morphology information while achieving efficient modular communication, MG2 introduces graph features three-levels, local, global, and relative graph features, and incorporates them into the transformer architecture. By introducing morphology prior information, MG2 improves multi-task training and generalization performance in morphology-generalizable reinforcement learning. The performance enhancements are evaluated primarily on the SMP benchmark and consolidated on several UNIMAL robots.