Interval POMDP Shielding for Imperfect-Perception Agents
📰 ArXiv cs.AI
Learn to shield imperfect-perception agents from unsafe decisions using interval POMDP shielding, ensuring reliable autonomous system operation
Action Steps
- Build a POMDP model of the autonomous system using known dynamics and estimated perception uncertainty
- Estimate confidence intervals for perception outcome probabilities from finite labeled data
- Implement an interval POMDP shielding algorithm to block potentially unsafe actions
- Test the shielding algorithm using simulated scenarios with varying perception uncertainty
- Apply the shielding technique to real-world autonomous systems to improve safety and reliability
Who Needs to Know This
Researchers and engineers working on autonomous systems with imperfect perception can benefit from this technique to improve safety and reliability
Key Insight
💡 Shielding can prevent unsafe decisions in autonomous systems with imperfect perception by estimating perception uncertainty and blocking potentially unsafe actions
Share This
🚀 Improve autonomous system safety with interval POMDP shielding for imperfect-perception agents! 🤖
Key Takeaways
Learn to shield imperfect-perception agents from unsafe decisions using interval POMDP shielding, ensuring reliable autonomous system operation
Full Article
Title: Interval POMDP Shielding for Imperfect-Perception Agents
Abstract:
arXiv:2604.20728v1 Announce Type: new Abstract: Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified. We study shielding for this setting: given a proposed action, a shield blocks actions that could violate safety. We consider the common case where system dynamics are known but perception uncertainty must be estimated from finite labeled data. From these data we build confidence intervals for the probabilities of perception outcomes
Abstract:
arXiv:2604.20728v1 Announce Type: new Abstract: Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified. We study shielding for this setting: given a proposed action, a shield blocks actions that could violate safety. We consider the common case where system dynamics are known but perception uncertainty must be estimated from finite labeled data. From these data we build confidence intervals for the probabilities of perception outcomes
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