# RoboFoundry

RoboFoundry proposes a Self-Evolving System-as-Policy framework that treats the entire supporting system around a foundation model—its context system and hierarchical skill system—rather than model parameters as the policy to evolve, diagnosing capability gaps from execution traces, converting them into validated task-specific system updates, and promoting recurring improvements into general system capabilities; it achieves state-of-the-art results on EmbodiedBench (improving GPT-5.5 by 27.8% and bringing Qwen3.7-Plus to 70.3% versus GPT-5.5's 72.7%), outperforms all baselines on RoboMemArena long-horizon memory by at least 39.0%, beats Cap-Agent0 on LIBERO-PRO by 243.8%–679.7%, and demonstrates zero-shot transfer and online evolution on real robots.

RoboFoundry proposes a Self-Evolving System-as-Policy framework that treats the entire supporting system around a foundation model—its context system and hierarchical skill system—rather than model parameters as the policy to evolve, diagnosing capability gaps from execution traces, converting them into validated task-specific system updates, and promoting recurring improvements into general system capabilities; it achieves state-of-the-art results on EmbodiedBench (improving GPT-5.5 by 27.8% and bringing Qwen3.7-Plus to 70.3% versus GPT-5.5's 72.7%), outperforms all baselines on RoboMemArena long-horizon memory by at least 39.0%, beats Cap-Agent0 on LIBERO-PRO by 243.8%–679.7%, and demonstrates zero-shot transfer and online evolution on real robots.