Humanoid loco-manipulationExpert
InterEvolve: Test-Time Evolution of Reward Programs for Humanoid Loco-Manipulation
InterEvolve is a preprint framework that evolves staged reward programs at test time so a frozen humanoid controller can solve new loco-manipulation tasks without retraining. It raises average success from 34.6% for the best calibrated fixed reward to 86.5% across eight goal-conditioned task families and transfers evolved skills to a real Unitree G1 using onboard perception. This turns task adaptation into a search over objectives rather than policy retraining, with more test-time compute improving results.