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

When teams of humans and robots work together to complete a task, communication is often necessary. For instance, imagine that you are working with a robot partner to move a table, and you notice that your partner is about to back into an obstacle they cannot see. One option is explicitly communicating with your teammate by telling them about the obstacle. But humans utilize more than just language—we also implicitly communicate through our actions. Returning to the example, we might physically guide our teammate away from the obstacle, and leverage our own forces to intuitively inform them about what we have observed. In this blog post, we explore how robot teams should harness the implicit communication contained within actions to learn about the world. We introduce a collaborative strategy where each robot alternates roles within the team, and demonstrate that roles enable accurate and useful communication. Our results suggest that teams which implicitly communicate with roles can match the optimal behavior of teams that explicitly communicate via messages. You can find our original paper on this research here.