An n-dimensional hybrid system embeds into a continuous vector field in m > 2n dimensions, enabling latent Neural ODEs with consistency losses to recover hybrid flows from time series.
Moment state dynamical sys- tems for nonlinear chance-constrained motion planning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
LieIPM applies a structure-preserving interior point optimizer to rigid-body trajectory planning on Lie groups using variational integrators and closed-form intrinsic derivatives.
CC-VPSTO formulates stochastic trajectory optimization as a chance-constrained problem, approximates it with Monte Carlo sampling and padding, and integrates it into MPC for online robot motion planning under uncertainty.
citing papers explorer
-
Embedding Hybrid Systems into Continuous Latent Vector Fields
An n-dimensional hybrid system embeds into a continuous vector field in m > 2n dimensions, enabling latent Neural ODEs with consistency losses to recover hybrid flows from time series.
-
LieIPM: Lie Group Interior Point Method for Direct Trajectory Optimization of Rigid Bodies
LieIPM applies a structure-preserving interior point optimizer to rigid-body trajectory planning on Lie groups using variational integrators and closed-form intrinsic derivatives.
-
CC-VPSTO: Chance-Constrained Via-Point-Based Stochastic Trajectory Optimisation for Online Robot Motion Planning under Uncertainty
CC-VPSTO formulates stochastic trajectory optimization as a chance-constrained problem, approximates it with Monte Carlo sampling and padding, and integrates it into MPC for online robot motion planning under uncertainty.