CS-Pi: Constraint-Satisfying Flow Policy Sampling
Overview
This paper introduces CS-Pi, a novel sampling technique for flow-matching robot policies that enforces safety constraints at inference time without retraining. The method projects actions to the nearest safe state and adds noise scaled by the projection distance before resampling, balancing efficiency with action plausibility. Experiments on a 7-DoF shelf pick-and-place task and zero-shot Franka hardware deployment show CS-Pi achieves 81% and 55% safe success rates respectively, outperforming standard projection (55% and 40%) while maintaining plausible action distributions.
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Reported quantity · DoF of shelf pick-and-place: 7 other · Basis not reported
“ejection's plausibility. CS-Pi, our draw-project-renoise sampler, projects each draw to the nearest safe action, adds noise back scaled to how far the projection moved it, and re-runs the unmodified policy until a draw is safe. Replacing only the projection step extends CS-Pi to continuous costs. Across a 2D diagnostic, a 7-DoF shelf pick-and-place, an orientation preference, and a zero-shot Franka deployment, CS-Pi”
Exact source · revision 1Source owner not reported
Reported quantity · diagnostic dimensionality: 2 other · Basis not reported
“ejection's plausibility. CS-Pi, our draw-project-renoise sampler, projects each draw to the nearest safe action, adds noise back scaled to how far the projection moved it, and re-runs the unmodified policy until a draw is safe. Replacing only the projection step extends CS-Pi to continuous costs. Across a 2D diagnostic, a 7-DoF shelf pick-and-place, an orientation preference, and a zero-shot Franka deployment, CS-Pi”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalCS-Pi: Constraint-Satisfying Flow Policy Sampling
This paper introduces CS-Pi, a novel sampling technique for flow-matching robot policies that enforces safety constraints at inference time without retraining. The method projects actions to the nearest safe state and adds noise scaled by the projection distance before resampling, balancing efficiency with action plausibility. Experiments on a 7-DoF shelf pick-and-place task and zero-shot Franka hardware deployment show CS-Pi achieves 81% and 55% safe success rates respectively, outperforming standard projection (55% and 40%) while maintaining plausible action distributions.
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