Teamformer: Scalable Heterogeneous Multi-Robot Team Formation

Abstract

Accounting for heterogeneity among robots and tasks adds additional complexity to multi-robot task allocation. While existing task allocation methods effectively handle heterogeneity among robots and tasks, they do not scale well in the number of different robots and tasks. To address this gap, we formulate the Team Formation Markov Decision Process (TF-MDP) for training Teamformer: a scalable, decentralized transformer policy for dynamically forming heterogeneous teams of robots to complete diverse tasks. Combining the TF-MDP with the autoregressive capability of transformers enables Teamformer to scale linearly in the number of robots, tasks, and combinations of different heterogeneous robots. Simulations demonstrate Teamformer generalizing to combinations of 100 different types of robots and tasks. Hardware experiments using Georgia Tech's Robotarium show Teamformer decentrally coordinating up to 20 heterogeneous robots for task completion.