Pushing Your Robot Policy toward Its Speed Limits via Coupled Gain–Duration Optimization

Robot policies learned from demonstrations can perform increasingly complex tasks with high reliability, but their execution speed often remains constrained by the demonstrations from which they are learned. We argue that an important bottleneck lies one level below the policy—the low-level controller that determines how quickly and how long each action is physically realized. We introduce Coupled Gain–Duration Optimization (CGDO), which accelerates pretrained policies by jointly optimizing the controller gain and execution duration for each action with reinforcement learning (RL) without modifying the policy. These two variables respectively govern how aggressively the commanded action target is approached and how long the action is executed, and their effects are tightly coupled, making joint optimization critical for pushing execution toward the speed limits without sacrificing task performance. CGDO is policy-agnostic by design and can be combined with other RL methods for further acceleration. Across a suite of experiments, CGDO achieves substantially shorter execution times than other compared methods, while in most cases can also improve success rates.