  Multi-robot manipulation tasks involve various control entities that can be
separated into dynamically independent parts. A typical example of such
real-world tasks is dual-arm manipulation. Learning to naively solve such tasks
with reinforcement learning is often unfeasible due to the sample complexity
and exploration requirements growing with the dimensionality of the action and
state spaces. Instead, we would like to handle such environments as multi-agent
systems and have several agents control parts of the whole. However,
decentralizing the generation of actions requires coordination across agents
through a channel limited to information central to the task. This paper
proposes an approach to coordinating multi-robot manipulation through learned
latent action spaces that are shared across different agents. We validate our
method in simulated multi-robot manipulation tasks and demonstrate improvement
over previous baselines in terms of sample efficiency and learning performance.
