EmbodiedRSI uses a hypothesis graph and value-of-information experiment selection to lift a frozen VLA to 77.0% on RoboCasa365 and 86.8% on LIBERO-Pro, transferring zero-shot to a real robot at 71.3%
EmbodiedRSI is a self-evolving agentic harness that, around a frozen robot foundation model, maintains code and skill hypotheses in a Hypothesis Graph, uses Value-of-Information Experiment Selection to pick the physical experiments that best distinguish them, and turns each interaction into reusable improvement through Code-Skill Co-Evolution and Reward-Grounded Memory Learning; it reaches 77.0% overall success on RoboCasa365 and 71.3% on Composite-Unseen (best baseline 40.1%), 86.8% overall on LIBERO-Pro, and transfers the simulation-evolved harness zero-shot to a real robot at 71.3% overall success.