HiFOR hierarchical foresight with action-guided world grounding
Original title: HiFOR: Hierarchical Foresight with Action-Guided World Grounding for Vision-Language-Action Model
HiFOR jointly predicts short-term states semantic milestones and goals using hierarchical dependency mask and action-guided retrieval for vision-language-action models.
VLA models integrate perception language and action but often separate short-term dynamics from long-term objectives. HiFOR uses hierarchical foresight prediction with dependency masks for multiple horizons. Structure-aware latent world modeling organizes representations along temporal view and spatial dimensions. Action-guided retrieval provides targeted future information during action generation.
- Hierarchical Dependency Mask establishes temporal dependencies across horizons
- Structure-Aware Latent World Modeling organizes future representations
- Action-Guided Factorized World Retrieval adapts retrieval based on action representations
- Effectiveness shown on two simulation benchmarks and six real-world tasks
- Outperforms existing methods in robotic manipulation
From the original article
1 min read at huggingface.co
Vision-Language-Action (VLA) models have demonstrated promising capabilities in robotic manipulation by integrating visual perception, language understanding, and action generation. Recent advances have further incorporated future prediction to enhance the understanding of environmental dynamics and task progression.
By Rongxing Ding, Hongyu Qu, Kehong Wu · huggingface.co
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