IMPFM is a multi-particle flow-map sampling method with sequential posterior sharing and interaction-aware correction that targets a KL-tilted distribution for global exploration in online feedback search.
Feed- back efficient online fine-tuning of diffusion models.arXiv preprint arXiv:2402.16359, 2024
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DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.
PAPA directly optimizes diffusion models via real-time user feedback for personalized preference alignment, drawing from variational inference, with an efficiency-enhanced variant EPAPA.
citing papers explorer
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Sequentially-Controlled Interactive Multi-Particle Flow-Maps for Online Feedback-Driven Search
IMPFM is a multi-particle flow-map sampling method with sequential posterior sharing and interaction-aware correction that targets a KL-tilted distribution for global exploration in online feedback search.
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DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.
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PAPA: Online Personalized Active Preference Alignment
PAPA directly optimizes diffusion models via real-time user feedback for personalized preference alignment, drawing from variational inference, with an efficiency-enhanced variant EPAPA.