Q-learning safety filter makes robots smarter about avoiding dangerous actions
This paper introduces a model-free safety filter using Q-learning that can protect any robotic system from unsafe actions, without requiring complex system dynamics knowledge. The filter learns to identify and prevent potentially dangerous actions while allowing task-specific policies to operate normally, making robotics safer and more reliable.
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https://arxiv.org/abs/2411.19809
🤖 Original Problem:
Ensuring safety in real-world robotics is challenging when system dynamics are complex or unknown. Existing safety approaches either need detailed system models or require significant modifications to reinforcement learning algorithms.
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🔧 Solution in this Paper:
→ The paper proposes a Q-learning based safety filter that learns to identify unsafe actions through a novel reward formulation.
→ The system uses two separate policies - one for the task and one for safety, trained simultaneously but independently.
→ A threshold-based filtering mechanism blocks potentially unsafe actions from the task policy and replaces them with safe alternatives.
→ The safety filter can be plugged into any existing reinforcement learning setup without requiring modifications.
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💡 Key Insights:
→ Model-free approaches can effectively ensure safety without system dynamics knowledge
→ Separating task and safety policies allows for better generalization
→ Threshold tuning provides a way to balance safety and performance
→ The approach works with both simple and complex robotic systems
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📊 Results:
→ Achieved 100% safety rate while maintaining highest average episodic return compared to existing methods
→ Successfully validated on double integrator and Dubin's car simulations
→ Demonstrated effectiveness on real-world soft robotic systems
