Robot Failures Became New Simulation Lessons

F4R automatically identifies a real-world failure, reconstructs the relevant object-centric tabletop conditions in simulation, co-trains on simulation and real data, and applies targeted reinforcement learning before redeployment. Across four manipulation tasks, the authors report 93.75% in-distribution and 90% out-of-distribution success, beating a budget-matched targeted behavior-cloning baseline by 18.75 percentage points out of distribution without collecting new real-world corrective demonstrations. The closed-loop evidence is limited to the reported tasks and reconstruction pipeline.