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[2602.21119] Cooperative-Competitive Team Play of Real-World Craft Robots
Summary
The paper explores advancements in multi-agent reinforcement learning for training cooperative and competitive robots, introducing a novel technique to enhance real-world application performance.
Why It Matters
This research addresses critical challenges in robotics, particularly the transfer of learned behaviors from simulations to real-world scenarios. By improving the efficiency of training collective robots, it has implications for various applications in automation and AI-driven systems.
Key Takeaways
- Introduces Out of Distribution State Initialization (OODSI) to improve sim-to-real transfer.
- Demonstrates a 20% performance improvement in real-world applications.
- Focuses on both cooperative and competitive multi-robot scenarios.
Computer Science > Robotics arXiv:2602.21119 (cs) [Submitted on 24 Feb 2026] Title:Cooperative-Competitive Team Play of Real-World Craft Robots Authors:Rui Zhao, Xihui Li, Yizheng Zhang, Yuzhen Liu, Zhong Zhang, Yufeng Zhang, Cheng Zhou, Zhengyou Zhang, Lei Han View a PDF of the paper titled Cooperative-Competitive Team Play of Real-World Craft Robots, by Rui Zhao and 8 other authors View PDF HTML (experimental) Abstract:Multi-agent deep Reinforcement Learning (RL) has made significant progress in developing intelligent game-playing agents in recent years. However, the efficient training of collective robots using multi-agent RL and the transfer of learned policies to real-world applications remain open research questions. In this work, we first develop a comprehensive robotic system, including simulation, distributed learning framework, and physical robot components. We then propose and evaluate reinforcement learning techniques designed for efficient training of cooperative and competitive policies on this platform. To address the challenges of multi-agent sim-to-real transfer, we introduce Out of Distribution State Initialization (OODSI) to mitigate the impact of the sim-to-real gap. In the experiments, OODSI improves the Sim2Real performance by 20%. We demonstrate the effectiveness of our approach through experiments with a multi-robot car competitive game and a cooperative task in real-world settings. Comments: Subjects: Robotics (cs.RO); Artificial Intelligence (cs.A...