This repository contains the model weights for AtlasVA, as presented in the paper AtlasVA: Self-Evolving Visual Skill Memory for Teacher-Free VLM Agents.

AtlasVA is a teacher-free visual skill memory framework designed for Vision-Language Model (VLM) agents. It organizes memory into three complementary layers: spatial heatmaps, visual exemplars, and symbolic text skills. By evolving danger and affinity atlases directly from trajectory statistics, AtlasVA provides dense, coordinate-aware guidance for reinforcement learning, unifying perception, memory, and optimization without external LLM supervision.