Goal-conditioned neural ODEs built from bi-Lipschitz diffeomorphisms deliver global exponential stability and safe-set invariance for all-pairs motion planning with explicit convergence bounds.
Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A unified three-stage framework learns trajectories and variable impedance from single demonstrations, manages singularities via null-space optimization for intuitive kinesthetic teaching, and adds foundational whole-body compliance for generalized safety on a 7-DOF robot.
citing papers explorer
-
Goal-Conditioned Neural ODEs with Guaranteed Safety and Stability for Learning-Based All-Pairs Motion Planning
Goal-conditioned neural ODEs built from bi-Lipschitz diffeomorphisms deliver global exponential stability and safe-set invariance for all-pairs motion planning with explicit convergence bounds.
-
A Unified Multi-Layer Framework for Skill Acquisition from Imperfect Human Demonstrations
A unified three-stage framework learns trajectories and variable impedance from single demonstrations, manages singularities via null-space optimization for intuitive kinesthetic teaching, and adds foundational whole-body compliance for generalized safety on a 7-DOF robot.