VLA-Pro improves cross-task generalization in vision-language-action models by storing task-specific LoRA adapters as procedural memories and retrieving/fusing them at inference.
Roboflamingo-plus: Fusion of depth and rgb perception with vision-language models for enhanced robotic manipulation.arXiv preprint arXiv:2503.19510, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
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QuoVLA introduces a quotient-space framework that compresses VLM latents into action-sufficient representations via quantization and dual-branch design for better VLA generalization.
citing papers explorer
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VLA-Pro: Cross-Task Procedural Memory Transfer for Vision-Language-Action Models
VLA-Pro improves cross-task generalization in vision-language-action models by storing task-specific LoRA adapters as procedural memories and retrieving/fusing them at inference.
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QuoVLA: Quotient Space for Vision-Language-Action Models
QuoVLA introduces a quotient-space framework that compresses VLM latents into action-sufficient representations via quantization and dual-branch design for better VLA generalization.