HJ-SafeDMP learns a control barrier value function offline from demonstrations via finite-difference HJ recursion and uses it as a closed-form safety filter on DMP outputs, with conformal prediction for coverage guarantees.
A general Hamilton-Jacobi framework for non-linear state-constrained control problems,
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HJ-SafeDMP: Hamilton-Jacobi Reachability-Guided Dynamic Movement Primitives for Provably Safe Robot Motion
HJ-SafeDMP learns a control barrier value function offline from demonstrations via finite-difference HJ recursion and uses it as a closed-form safety filter on DMP outputs, with conformal prediction for coverage guarantees.