[Paper Review] Learning from My Partner’s Actions: Roles in Decentralized Robot Teams

Dylan P. Losey, Mengxi Li+2 more|arXiv (Cornell University)|Oct 16, 2019

Social Robot Interaction and HRI7 citations

TL;DR

This paper proposes a decentralized robot teaming approach where agents assume distinct roles—such as exploit, communicate, or explore—to implicitly convey intent through actions, eliminating ambiguity in partner interpretation. By assigning roles that define specific action purposes, teams achieve performance comparable to explicitly communicating teams without message exchange.

ABSTRACT

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 this http URL.

Motivation & Objective

• To address the challenge of interpreting robot teammates' actions in decentralized teams, where actions may stem from multiple underlying intentions.
• To reduce ambiguity in action interpretation by assigning distinct roles to agents, each with a unique purpose for acting.
• To enable effective collaboration in decentralized robot teams without relying on explicit communication channels.
• To evaluate whether role-based action interpretation can match the performance of teams using explicit message passing.

Proposed method

• Agents are assigned specific roles—such as exploit, communicate, or explore—each defining a unique reason for performing actions.
• Each role constrains the agent’s behavior to a single purpose, ensuring actions are unambiguously interpretable by teammates.
• The system enables role alternation and coordination through decentralized decision-making, without centralized control.
• Action interpretation is based solely on role assignment, not on inferred intent or context, simplifying teammate understanding.
• The approach avoids explicit communication by encoding intent directly into role-specific behaviors.
• Performance is evaluated in simulated and real-world robotic experiments comparing role-based teams to message-passing teams.

Experimental results

Research questions

• RQ1 Can robots in decentralized teams implicitly communicate intent through role-specific actions without explicit messaging?
• RQ2 How does role-based action interpretation compare to traditional methods relying on intent inference?
• RQ3 Does assigning distinct roles lead to team performance comparable to explicitly communicating teams?
• RQ4 How do role alternation and assignment affect coordination and task completion efficiency?
• RQ5 What is the impact of role clarity on teammate action interpretation and team-wide performance?

Key findings

• Teams using role-based actions achieved performance comparable to teams that explicitly exchanged messages, demonstrating the effectiveness of implicit communication.
• The role-based approach reduced ambiguity in interpreting teammates' actions, as each action had a single, predefined purpose.
• Decentralized coordination was maintained without centralized coordination or explicit signaling, enabling scalable team operation.
• The method enabled reliable task completion in complex environments where intent inference would otherwise fail.
• Experimental results, including videos and images, validated the approach in both simulation and real-world robotic setups.
• Role alternation and clear behavioral constraints significantly improved team coordination and task efficiency.