Teaching robots to compose skills without relearning

Current robot learning systems treat skills as pure action sequences, missing the symbolic outcomes that make composition possible. PACTS models each skill as a joint distribution over both actions and their symbolic effects, letting a single policy generate coherent action-outcome rollouts. At test time, the model predicts symbolic outcomes in real time, enabling a planner to sequence skills together without ever seeing that combination before.