This paper proposes a new approach to robotic manipulation planning based on the contact between a set of objects, robots and surfaces. We consider making or breaking contact as the most abstract, yet representative element of a manipulation task. Using this paradigm, a robotic manipulation planner has been developed. Given an environment with robots and objects, a manipulation graph is generated by a set of rules and the available geometrical information. Next, the object manipulation planning is formulated as a graph search problem. Paths on this graph divide a complex manipulation task into sub-tasks, followed by low-level path planning and controller assignment for each sub-task. By sequentially executing these controllers in a hybrid fashion, one achieves the overall manipulation task.

Sequential composition is a supervisory control architecture for addressing control problems in complex dynamical systems. Although sequential composition works properly for a single system, it is not designed for cooperative systems. This paper extends the standard sequential composition by introducing a novel approach to compose multiple sequential composition controllers towards cooperative control. Given two or more systems, cooperation is achieved by composing each of the systems' control automaton, together with estimation for the domains of attraction of the resulting composed controllers. This typically results in new events for the original sequential composition controllers. Applying these events, the cooperative control system can fulfill the tasks which are not possible to satisfy with the original controllers individually. The simulation results of an inverted pendulum system collaborating with two second-order DC motors are presented for cooperative swing-up maneuvers.

Most stabilizing controllers designed for nonlinear systems are valid only within a specific region of the state space, called the domain of attraction (DoA). Computation of the DoA is usually costly and time-consuming. This paper proposes a computationally effective sampling approach to estimate the DoAs of nonlinear systems in real time. This method is validated to approximate the DoAs of stable equilibria in several nonlinear systems. In addition, it is implemented for the passivity-based learning controller designed for a second-order dynamical system. Simulation and experimental results show that, in all cases studied, the proposed sampling technique quickly estimates the DoAs, corroborating its suitability for real-time applications.