A neural BRAT framework trained with curriculum-driven MPC supervision approximates HJ reach-avoid value functions and deploys them via gradient and augmented-MPC controllers, outperforming baselines on 6D and 13D docking tasks.
Control barrier functions: Theory and applications
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
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.
citing papers explorer
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Neural Backward Reach-Avoid Tubes with MPC Supervision for High-Dimensional Systems: An Application to Safe Spacecraft Docking
A neural BRAT framework trained with curriculum-driven MPC supervision approximates HJ reach-avoid value functions and deploys them via gradient and augmented-MPC controllers, outperforming baselines on 6D and 13D docking tasks.
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A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation
A hybrid navigation system uses offline HJ reachability computations as heuristics and safety constraints within graph search to achieve faster and safer robot movement in complex indoor environments.