LAMP extracts continuous 3D inter-object transformations from image editing to serve as geometry-aware priors for zero-shot open-world robotic manipulation.
Model-based reinforcement learn- ing: A survey.Foundations and Trends® in Machine Learn- ing, 16(1):1–118, 2023
2 Pith papers cite this work. Polarity classification is still indexing.
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IPR uses world-model rollouts to reinforce a VLM policy via PhysCode on a 1000+ game benchmark, achieving robust physical reasoning that improves with experience and transfers zero-shot to unseen games while surpassing GPT-5.
citing papers explorer
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LAMP: Lift Image-Editing as General 3D Priors for Open-world Manipulation
LAMP extracts continuous 3D inter-object transformations from image editing to serve as geometry-aware priors for zero-shot open-world robotic manipulation.
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IPR-1: Interactive Physical Reasoner
IPR uses world-model rollouts to reinforce a VLM policy via PhysCode on a 1000+ game benchmark, achieving robust physical reasoning that improves with experience and transfers zero-shot to unseen games while surpassing GPT-5.