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When teams of robots collaborate to complete a task, communication is oftennecessary. Like humans, robot teammates should implicitly communicate throughtheir actions: but interpreting our partner's actions is typically difficult,since a given action may have many different underlying reasons. Here wepropose an alternate approach: instead of not being able to infer whether anaction is due to exploration, exploitation, or communication, we defineseparate roles for each agent. Because each role defines a distinct reason foracting (e.g., only exploit, only communicate), teammates now correctlyinterpret the meaning behind their partner's actions. Our results suggest thatleveraging and alternating roles leads to performance comparable to teams thatexplicitly exchange messages. You can find more images and videos of ourexperimental setups at http://ai.stanford.edu/blog/learning-from-partners/.

Learning from My Partner's Actions: Roles in Decentralized Robot Teams