Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
Neural ordinary differential equations
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
ICODE-MPPI uses Input Concomitant Neural ODEs to learn residual dynamics and reduce vehicle cross-tracking error by up to 69% under disturbances compared with standard MPPI.
LASS-ODE-Power is a pretrained model that predicts power-system dynamic trajectories across regimes in a zero-shot manner after large-scale ODE pretraining and targeted fine-tuning.
Meta-learning framework adapting iMAML for rapid controller tuning on uncertain nonlinear systems via offline source data and limited online target adaptation, shown with neural state-space and DQN variants.
PDSL integrates deep generative models with Graph Neural ODEs to model stochastic diffusion dynamics and improve source localization accuracy over prior topology-focused methods.
An online learning-enhanced high-order adaptive CBF with Neural ODEs maintains safety for a 38g nano quadrotor against 18km/h wind by adapting to time-varying perturbations on the fly.
citing papers explorer
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Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models
Flow Motion Policy uses flow matching to model distributions over feasible manipulator paths, enabling best-of-N sampling with post-generation collision filtering to improve success and efficiency over prior neural and sampling-based planners.
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Robust Path Tracking for Vehicles via Continuous-Time Residual Learning: An ICODE-MPPI Approach
ICODE-MPPI uses Input Concomitant Neural ODEs to learn residual dynamics and reduce vehicle cross-tracking error by up to 69% under disturbances compared with standard MPPI.
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Predicting Power-System Dynamic Trajectories with Foundation Models
LASS-ODE-Power is a pretrained model that predicts power-system dynamic trajectories across regimes in a zero-shot manner after large-scale ODE pretraining and targeted fine-tuning.
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Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
Meta-learning framework adapting iMAML for rapid controller tuning on uncertain nonlinear systems via offline source data and limited online target adaptation, shown with neural state-space and DQN variants.
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PDSL: Propagation Dynamics Aware Framework for Source Localization
PDSL integrates deep generative models with Graph Neural ODEs to model stochastic diffusion dynamics and improve source localization accuracy over prior topology-focused methods.
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Online Learning-Enhanced High Order Adaptive Safety Control
An online learning-enhanced high-order adaptive CBF with Neural ODEs maintains safety for a 38g nano quadrotor against 18km/h wind by adapting to time-varying perturbations on the fly.