RoboRSI: Evolving a guidance Strategy for Robotic Self-Improvement

Robotic self-improvement requires using history experience to determine which system improvements are worth pursuing. Evolving the execution system alone does not establish persistent guidance for selecting and reviewing improvements across successive rounds. We introduce RoboRSI, a robotic self-improvement framework that learns and evolves an explicit system-level strategy: a continually updated set of rules for selecting and reviewing improvements to task-planning guidance and robot skills. RoboRSI learns this strategy from historical traces linking execution outcomes, improvement decisions and their effects, and validates strategy updates through the downstream improvements they induce on held-out cases. Across four robotic benchmarks, RoboRSI improves task success rate by an average of 15.4 percentage over its no-strategy counterpart. Controlled ablations show an 8.2 percentage gain from maintaining the strategy under matched access to historical experience on LIBERO-Pro, and a 10.6 percentage gain from continually updating the strategy over keeping it fixed on RoboCasa365.