Learning from My Partner's Actions: Roles in Decentralized Robot Teams
This addresses coordination challenges in multi-robot systems, offering an implicit communication alternative, though it appears incremental.
The paper tackles the problem of interpreting partner actions in decentralized robot teams by assigning distinct roles to each agent, which clarifies the reasons behind actions and leads to performance comparable to explicit communication.
When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpreting our partner's actions is typically difficult, since a given action may have many different underlying reasons. Here we propose an alternate approach: instead of not being able to infer whether an action is due to exploration, exploitation, or communication, we define separate roles for each agent. Because each role defines a distinct reason for acting (e.g., only exploit, only communicate), teammates now correctly interpret the meaning behind their partner's actions. Our results suggest that leveraging and alternating roles leads to performance comparable to teams that explicitly exchange messages. You can find more images and videos of our experimental setups at http://ai.stanford.edu/blog/learning-from-partners/.