Energy-based Policy Orchestration

Hierarchical control scales robot behavior by separating task-level planning from low-level control. In dynamic, high-dimensional tasks, however, a fixed planner-to-policy interface can fail when contacts, balance, or disturbances evolve faster than high-level policy updates. Execution therefore requires choosing not only which low-level policy to run, but also which policy-specific goal should be given to it.

We formulate this problem as policy orchestration: runtime selection of a policy–goal pair that is likely to remain feasible while tracking the planner target. We develop EPO, an energy-based policy orchestrator for modular goal-conditioned policy families. For each policy, EPO learns a composite energy over low-level goals, where lower energy corresponds to higher feasibility and more accurate target realization. At runtime, EPO refines each policy's goal and executes the lowest-energy policy–goal pair.

Because energies are policy-specific, new policies can be incorporated by learning their own energy models, without retraining existing policies or a centralized scheduler. In simulation, EPO improves the empirical precision–robustness trade-off by refining policy-specific goals before selectively switching among policies based on energy scores. We further demonstrate sim-to-real deployment of the same frozen policies and energy models on real humanoid loco-manipulation tasks.