IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control
arXiv:2606.26575v1 Announce Type: cross Abstract: Complex multi-agent control tasks remain challenging for traditional rule-based and model-based approaches, motivating the adoption of learning-based methods. However, learning-based methods often struggle with sim-to-real transfer because they rely on accurate dynamics modeling or system identification and learn policies in low-level control spaces that are highly sensitive to dynamics mismatch, making them costly and fragile in complex environments. To address this issue, we propose a sim-to-real method for multi-agent control, which is insen
The increasing complexity of multi-agent control tasks in both simulated and real-world environments necessitates more robust and reliable methods for transferring learned policies.
This research addresses a critical barrier in AI development, enabling effective deployment of learning-based control systems from simulation to reality, specifically impacting robotics and autonomous systems.
The proposed 'Effect Alignment' method reduces the sensitivity of learning-based multi-agent control policies to discrepancies between simulated and real-world dynamics, improving system reliability and deployment efficiency.
- · Robotics companies
- · Autonomous systems developers
- · Logistics and manufacturing automation
- · AI research institutions
- · Companies relying solely on rule-based control systems
- · Legacy simulation software lacking robust sim-to-real transfer features
Improved sim-to-real transfer directly accelerates the development and deployment of complex multi-agent robotic systems.
More reliable autonomous multi-agent systems could lead to significant advancements in areas like automated factories, military drone swarms, and smart city infrastructure.
The reduced need for extensive real-world testing could dramatically lower the cost and increase the speed of innovation in advanced robotics, democratizing access to complex autonomous capabilities.
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Read at arXiv cs.AI