MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation

Understand

Pre-trained Vision-Language-Action (VLA) models have achieved remarkable success in improving robustness and generalization for end-to-end robotic manipulation.

• However, these models struggle with long-horizon tasks due to their lack of memory and reliance solely on immediate sensory inputs.
• To address this limitation, we propose Memory-Augmented Prompting for Vision-Language-Action model (MAP-VLA), a novel framework that empowers pre-trained VLA models with demonstration-derived memory prompts to augment action generation for long-horizon robotic manipulation tasks.
• To achieve this, MAP-VLA first constructs a memory library from historical demonstrations, where each memory unit captures information about a specific stage of a task.