ACRO: Actor-Critic Rollout Orchestration for Exploiting VLA Capability Bounds

Learning to act leaves open the question of when a robot should continue executing its policy and when it should intervene. We introduce ACRO, an Actor–Critic rollout orchestration layer that improves execution of a fixed vision-language-action policy. A learned Critic monitors changes in the success prospects of the policy's proposed actions. When intervention is needed, Trajectory Retraction reconstructs the execution path into a constrained search region, and a learned state-value function selects a promising continuation point. The Actor moves the robot toward that configuration and returns control to the policy. We formulate intervention through the difference between the value of continuing the current action and resuming after a correction. Across SIMPLER, RoboCasa, and real-robot manipulation, ACRO improves success by 10.4, 7.3, and 21.7 percentage points, respectively. These results show how execution-time orchestration can make more effective use of an existing policy's capabilities.