This article presents the concept of model predictive interaction control (MPIC) as a generic, flexible, and comprehensive approach for robotic manipulation tasks. MPIC is based on the repetitive solution of an optimal control problem that includes a robot model for motion prediction as well as an interaction model for force prediction. In order to handle both elastic and rigid contact situations, a cascaded approach with low-level PD control is adopted, which allows to combine the linear-elastic environment model and the limited controller stiffness. Due to its flexibility, MPIC can be favorably used for realizing the elementary manipulation primitives (MP) within a hierarchical task planning framework, where each MP corresponds to a particular parameterization of the cost function and the constraints. The control methodology and the manipulation approach are evaluated in simulations and experiments using a 7-degree-of-freedom industrial robot.

The development of modern sensitive lightweight robots allows the use of robot arms in numerous new scenarios. Especially in applications where interaction between the robot and an object is desired, e.g. in assembly, conventional purely position-controlled robots fail. Former research has focused, among others, on control methods that center on robot-environment interaction. However, these methods often consider only separate scenarios, as for example a pure force control scenario. The present paper aims to address this drawback and proposes a control framework for robot-environment interaction that allows a wide range of possible interaction types. At the same time, the approach can be used for setpoint generation of position-controlled robot arms, where no interaction takes place. Thus, switching between different controller types for specific interaction kinds is not necessary. This versatility is achieved by a model predictive control-based framework which allows trajectory following control of joint or end-effector position as well as of forces for compliant or rigid robot-environment interactions. For this purpose, the robot motion is predicted by an approximated dynamic model and the force behavior by an interaction model. The characteristics of the approach are discussed on the basis of two scenarios on a lightweight robot.