  Effective communication is an important skill for enabling information
exchange and cooperation in multi-agent settings. Indeed, emergent
communication is now a vibrant field of research, with common settings
involving discrete cheap-talk channels. One limitation of this setting is that
it does not allow for the emergent protocols to generalize beyond the training
partners. Furthermore, so far emergent communication has primarily focused on
the use of symbolic channels. In this work, we extend this line of work to a
new modality, by studying agents that learn to communicate via actuating their
joints in a 3D environment. We show that under realistic assumptions, a
non-uniform distribution of intents and a common-knowledge energy cost, these
agents can find protocols that generalize to novel partners. We also explore
and analyze specific difficulties associated with finding these solutions in
practice. Finally, we propose and evaluate initial training improvements to
address these challenges, involving both specific training curricula and
providing the latent feature that can be coordinated on during training.
