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Proactive collaboration via autonomous interaction
Peiyan Li1, Shuyuan Zhang1, Wenju Yang2
1Department of Computer Science and Technology, BNRIST, Tsinghua University, Beijing, China.
Nature Communications
|May 9, 2026
Summary
This study introduces Proactive Collaboration for robot teams, enabling them to autonomously adapt team composition and task assignments. This leads to improved efficiency and robustness in multi-robot systems.
Area of Science:
- Robotics
- Artificial Intelligence
- Multi-Agent Systems
Background:
- Human teams dynamically adapt composition and tasks, a key to effectiveness.
- Multi-robot systems often lack this adaptive capacity, using fixed configurations or reactive adjustments.
Purpose of the Study:
- To investigate the Proactive Collaboration paradigm for robot teams.
- To develop a framework enabling autonomous adaptation of team composition and task allocation.
Main Methods:
- Implemented the Proactive Collaboration paradigm using an Autonomous Interaction framework.
- Utilized need-driven, multi-round communication for task discussion, allocation, and team resizing.
- Conducted experiments in both real-world and simulated environments.
Main Results:
- Demonstrated effective realization of Proactive Collaboration in robot teams.
- Achieved measurable gains in team efficiency and robustness through anticipatory planning.
- Resolved capability gaps and minimized action redundancy.
Conclusions:
- Enabling individual-level initiative in robots promotes adaptive and cohesive collective behavior.
- Proactive Collaboration offers a promising approach for enhancing multi-robot system performance.
- The Autonomous Interaction framework facilitates dynamic team resizing and task negotiation.
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