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.
Motion planning diffusion: Learning and adapting robot motion planning with diffusion models
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HardFlow turns hard constraint enforcement during flow-matching sampling into a tractable terminal-time trajectory optimization problem using optimal control.
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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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.