Heracles: Bridging Precise Tracking and Generative Synthesis for General Humanoid Control

Achieving general-purpose humanoid control requires a delicate balance between the precise execution of commanded motions and the flexible, anthropomorphic realignment of state and intent. Current general controllers predominantly formulate motion control as a rigid reference-tracking problem. This formulation implicitly assumes that the reference is always reachable from the current state; once this assumption breaks, these trackers produce myopic, single-step corrections and exhibit brittle, non-anthropomorphic failure modes, lacking the generative adaptability inherent to human motor control. To overcome this limitation, we propose Heracles, a novel state-conditioned generative middleware that bridges precise motion tracking and generative synthesis. Rather than relying on rigid tracking paradigms or explicit mode-switching between a tracker and a dedicated recovery policy, Heracles operates as an intermediary layer between high-level reference motions and low-level physics trackers. By conditioning on the robot's real-time state, the flow-matching model implicitly adapts its behavior: it approximates an identity map when the state closely aligns with the reference, preserving zero-shot tracking fidelity. Conversely, when encountering significant state deviations, it seamlessly transitions into a generative synthesizer, producing an anthropomorphic whole-body maneuver back toward the reference. Our framework demonstrates that integrating generative priors into the control loop not only significantly enhances robustness against extreme perturbations but also elevates humanoid control from a rigid tracking paradigm to a generative general-purpose architecture. Videos and demos are available at https://heracles-humanoid-control-submission.github.io