A modular belief-space controller using learned Belief Control Lyapunov Functions for information gathering and conformal-prediction Belief Control Barrier Functions for safety reduces reach-avoid POMDP synthesis to fast quadratic programs.
Barriernet: Differentiable control barrier functions for learning of safe robot control,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Integrates RL with a differentiable CVaR quadratic-program safety layer to jointly learn nominal controls, risk levels, and margins for adaptive safe navigation under motion uncertainty.
citing papers explorer
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Safety-critical Control Under Partial Observability: Reach-Avoid POMDP meets Belief Space Control
A modular belief-space controller using learned Belief Control Lyapunov Functions for information gathering and conformal-prediction Belief Control Barrier Functions for safety reduces reach-avoid POMDP synthesis to fast quadratic programs.
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Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
Integrates RL with a differentiable CVaR quadratic-program safety layer to jointly learn nominal controls, risk levels, and margins for adaptive safe navigation under motion uncertainty.