Kintsugi learns policies by repairing composable executable knowledge bases through agentic diagnosis, localized typed edits, and deterministic verification gates that admit only improvements.
Robot-dift: Distilling diffusion features for geometrically consistent visuomotor control
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LIDEA bridges the human-robot embodiment gap via implicit feature distillation in 2D and explicit geometry alignment in 3D, enabling human data to substitute up to 80% of robot demonstrations with improved out-of-distribution robustness.
citing papers explorer
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Kintsugi: Learning Policies by Repairing Executable Knowledge Bases
Kintsugi learns policies by repairing composable executable knowledge bases through agentic diagnosis, localized typed edits, and deterministic verification gates that admit only improvements.
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LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment
LIDEA bridges the human-robot embodiment gap via implicit feature distillation in 2D and explicit geometry alignment in 3D, enabling human data to substitute up to 80% of robot demonstrations with improved out-of-distribution robustness.