Flow matching robot policies trained in a continuous latent action space produce smoother, more successful long-horizon manipulation than raw-action-space flow matching, at near-single-step inference speed.
Fast and robust visuomotor riemannian flow matching policy
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
representative citing papers
HardFlow turns hard constraint enforcement during flow-matching sampling into a tractable terminal-time trajectory optimization problem using optimal control.
citing papers explorer
-
CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation
Flow matching robot policies trained in a continuous latent action space produce smoother, more successful long-horizon manipulation than raw-action-space flow matching, at near-single-step inference speed.
-
HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization
HardFlow turns hard constraint enforcement during flow-matching sampling into a tractable terminal-time trajectory optimization problem using optimal control.