Consensus-based Normalizing-Flow Control: A Case Study in Learning Dual-Arm Coordination

We develop two consensus-based learning algorithms for multi-robot systems applied on complex tasks involving collision constraints and force interactions, such as the cooperative peg-in-hole placement. The proposed algorithms integrate multi-robot distributed consensus and normalizing-flow-based reinforcement learning. The algorithms guarantee the stability and the consensus of the multi-robot system's generalized variables in a transformed space. This transformed space is obtained via a diffeomorphic transformation parameterized by normalizing-flow models that the algorithms use to train the underlying task, learning hence skillful, dexterous trajectories required for the task accomplishment. We validate the proposed algorithms by parameterizing reinforcement learning policies, demonstrating efficient cooperative learning, and strong generalization of dual-arm assembly skills in a dynamics-engine simulator.