You can dramatically improve robot performance by automating harness design—the control logic wrapper around a policy—rather than retraining the policy itself, achieving specialist-level performance from general-purpose models.
RoboArchitect automates the design of control harnesses around frozen robot policies using a coding model that observes failures and iteratively revises the harness code. Without updating model weights, it improves success rates 4.5x on unseen scenes and 3.2x on unseen tasks by automatically discovering strategies like obstacle avoidance and persistent memory for learned behaviors.