COP-Q: Safety-First Reinforcement Learning for Robot Control via Cholesky-Ordered Projection
arXiv:2606.04749v1 Announce Type: cross Abstract: Safe robot control requires maximizing return while satisfying safety constraints. In off-policy safe reinforcement learning, reward and safety Q-values are commonly learned by separate critic ensembles, with uncertainty handled independently for each objective. This objective-wise treatment neglects inter-objective correlation and can lead to overly conservative value estimates, thereby reducing sample efficiency. To address this issue, we propose Cholesky-Ordered Projection Q-learning (COP-Q), a safety-first method that incorporates inter-obj
The continuous drive for safer, more efficient AI in robotics necessitates innovations that address current limitations in reinforcement learning, especially for real-world applications.
This research contributes to making autonomous robot control more robust and safe, which is critical for broader adoption across industries and complex environments.
The proposed COP-Q method offers a more sample-efficient and less conservative approach to safe robot learning by accounting for correlations between reward and safety objectives.
- · Robotics manufacturers
- · Logistics and industrial automation sectors
- · AI/ML researchers in control systems
- · Inefficient reinforcement learning algorithms
- · Manual control systems in hazardous environments
Improved safety and efficiency in robotic operations through advanced AI control methods.
Accelerated deployment of autonomous robots in safety-critical sectors like healthcare, defense, and complex manufacturing.
Reduced operational costs and increased productivity across industries due to more reliable robotic automation, potentially impacting labor markets.
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Read at arXiv cs.LG