RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes
RoboAware learns how to coordinate different ways of controlling a robot's skills—modular composition versus end-to-end control—based on the current state. It uses counterfactual outcomes from simulations to assign responsibility between these methods. This allows the system to choose the best skill strategy dynamically at runtime, leading to high success rates in complex tasks.