Action Semantics Learning trains app agents to align with the semantic effects of actions via a Semantic Estimator module, improving robustness to out-of-distribution scenarios over syntax-matching fine-tuning.
Compositional foundation models for hierarchical planning.Advances in Neural Information Processing Systems, 36:22304–22325, 2023
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SWEET is a one-shot sparse visual planning framework that progressively generates manipulation keyframes via image editing conditioned on language and spatial guidance, then converts them to actions with a diffusion predictor, showing better fidelity and lower cost than video models on DROID and Rob
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Beyond Syntax: Action Semantics Learning for App Agents
Action Semantics Learning trains app agents to align with the semantic effects of actions via a Semantic Estimator module, improving robustness to out-of-distribution scenarios over syntax-matching fine-tuning.
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SWEET: Sparse World Modeling with Image Editing for Embodied Task Execution
SWEET is a one-shot sparse visual planning framework that progressively generates manipulation keyframes via image editing conditioned on language and spatial guidance, then converts them to actions with a diffusion predictor, showing better fidelity and lower cost than video models on DROID and Rob