A Robot's Fix Is Not Yet a Policy
TL;DR for operators When a robot succeeds after repairing a failed procedure, the next decision is whether that repair should remain local or become a reusable capability. RoboFoundry1 addresses this through system evolution without foundation-model weight updates. It diagnoses failures from execution traces, revises memory or execution support, and verifies proposed general changes before retaining them. On EmbodiedBench, GPT-5.5 average success increases from 56.9% to 72.7%. An ablation also supports the value of persistent general-system improvement beyond task-specific repair. Organizations deploying embodied agents can investigate this approach before pursuing model replacement, but shared revisions still require deployment-specific testing, safety controls, and cost evaluation. ...