A team from Nanyang Technological University, Beihang University, National University of Singapore, Shanghai Jiao Tong University and Yundie Technology proposes RoboFoundry, an embodied agent framework that formalizes the agent system itself as a "Self-Evolving System-as-Policy": base model parameters stay frozen while evolution happens in the context system, skill system and execution interface around the model, driven by an execute-reflect-repair-promote loop and a shared semantic interface for reuse across robots. On EmbodiedBench all five base models improved, with GPT-5.5's average success rate rising from 56.9% to 72.7%, above unevolved GPT-6 Astra at 71.9%; on the long-horizon memory benchmark RoboMemArena it reports task success at least 39.0% above all baselines, and it tops LIBERO-PRO perturbation tests at 91.9% average success. Real-robot experiments on an AgileX platform reached 70% success on a nesting-cups task versus 30% for π0.5 alone, alongside zero-shot skill transfer to unseen towels, semantic navigation on a Unitree G1, and online evolution after deployment.

RoboFoundry·Nanyang Technological University·Beihang University·+5