  The ability to communicate intention enables decentralized multi-agent robots
to collaborate while performing physical tasks. In this work, we present
spatial intention maps, a new intention representation for multi-agent
vision-based deep reinforcement learning that improves coordination between
decentralized mobile manipulators. In this representation, each agent's
intention is provided to other agents, and rendered into an overhead 2D map
aligned with visual observations. This synergizes with the recently proposed
spatial action maps framework, in which state and action representations are
spatially aligned, providing inductive biases that encourage emergent
cooperative behaviors requiring spatial coordination, such as passing objects
to each other or avoiding collisions. Experiments across a variety of
multi-agent environments, including heterogeneous robot teams with different
abilities (lifting, pushing, or throwing), show that incorporating spatial
intention maps improves performance for different mobile manipulation tasks
while significantly enhancing cooperative behaviors.
