AWhat changed
Learned a diffusion prior over state/action trajectories, guided reverse denoising with reward gradients or clamped constraints and executed sampled plans in a receding-horizon loop.
Scientific scope
Learned a diffusion prior over state/action trajectories, guided reverse denoising with reward gradients or clamped constraints and executed sampled plans in a receding-horizon loop. Guided conditional sampling is approximate and does not certify feasible or globally optimal plans. Variable-length convolutional horizons do not remove planning cost or guarantee arbitrary-horizon quality. Distinct from a diffusion single-action policy or later imitation-only robotics systems.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Guided conditional sampling is approximate and does not certify feasible or globally optimal plans. Variable-length convolutional horizons do not remove planning cost or guarantee arbitrary-horizon quality. Distinct from a diffusion single-action policy or later imitation-only robotics systems.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
Full scientific record · Browse contribution grades · Request a correction