Tag: AI planning
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Efficient Embodied World Models for AI Planning
Discover how lifted embodied world models enhance AI planning and control by simplifying high-dimensional actions for better robotics performance.
Self-Adaptive Hierarchical Planning for Efficient LLM Agents
Discover AdaPlan-H, a self-adaptive hierarchical planning method that improves LLM agents' task execution by balancing coarse and fine planning.
Probabilistic Framework for Hierarchical Goal Recognition AI
Discover a new probabilistic framework enhancing hierarchical goal recognition in AI, improving accuracy and handling uncertainty in complex tasks.
Learning Lifted Action Models from Visual State Sequences
Discover how lifted action models are learned from unsupervised visual state sequences using deep learning and MILP for AI planning improvements.
Mixed-Density Diffuser: Boost AI Planning Efficiency
Discover how Mixed-Density Diffuser optimizes AI planning with non-uniform temporal resolution, surpassing state-of-the-art benchmarks in reinforcement lea...