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Figure 1: Two agents learn to successfully navigate through a previously unseen environment to find, and jointly lift, a heavy TV. Without learned communication, agents attempt many failed actions and pickups. With learned communication, agents send a message when they observe or when they intend to interact with the TV. The agents also learn to grab the opposite ends of the TV and coordinate to do so. AbstractCollaboration is a necessary skill to perform tasks that are beyond one agent's capabilities. Addressed extensively in both conventional and modern AI, multi-agent collaboration has often been studied in the context of simple grid worlds. We argue that there are inherently visual aspects to collaboration which should be studied in visually rich environments. A key element in collaboration is communication that can be either explicit, through messages, or implicit, through perception of the other agents and the visual world. Learning to collaborate in a visual environment entails learning (1) to perform the task, (2) when and what to communicate, and (3) how to act based on these communications and the perception of the visual world. In this paper we study the problem of learning to collaborate directly from pixels in AI2-THOR and demonstrate the benefits of explicit and implicit modes of communication to perform visual tasks. Refer to our project page for more details: https://prior.allenai.org/projects/ two-body-problem * indicates equal contributions.