Effective human-robot cooperation requires robotic devices that understand human goals and intentions. We frame the problem of intent recognition as one of tracking and predicting human actions within the context of plan task sequences. A hybrid mode estimation approach, which estimates both discrete operating modes and continuous state, is used to accomplish this tracking based on possibly noisy sensor input. The operating modes correspond to plan tasks, hence, the ability to estimate and predict these provides a prediction of human actions and associated needs in the plan context. The discrete and continuous estimates interact in that the discrete mode selects continous dynamic models used in the continuous estimation, and the continuous state is used to evaluate guard conditions for mode transitions. Two applications: active prosthetic devices, and cooperative assembly, are described.