Widespread deployment of robots in offices, hospitals, and homes is a highly anticipated breakthrough in robotics. In such environments, the robots are expected to fulfil new tasks as they arrive in contrast to repetitive tasks. Such environments are unstructured and may impose various constraints on a robot's motion. Therefore, robots should be able to solve new instances of complex problems where kinematic and dynamical constraints must be respected. In this thesis we investigate how to autonomously incorporate these constraints in a robotic problem specification and how to develop motion planning and control techniques that solve such a problem.

The combination of environment-imposed constraints including obstacles, with a robot's own limitations, e.g., actuation bounds, results in many scenarios where a robotic task becomes a non-convex problem. This inhibits the commonly assumed independence between motion planning and control, making various classical control approaches practically infeasible. In the first part of this thesis, we introduce our contribution toward autonomous incorporation of kinematic and dynamical limitations at planning level by representing the robot action space as a set of feedback motion primitives. We show how the dual path planning and path non-existence problems can be solved for a mobile robot even in presence of external disturbance using feedback motion primitives. We further extend the applicability of motion primitives to long-horizon planning problems and address complex tasks specified as linear temporal logic (LTL) formulas by introducing a guided search scheme. Ultimately in this part, we lay a theoretical foundation for automated construction of such feedback motion primitives for a large class of systems by decomposing their state-space into smaller regions where locally linear controllers can be synthesized.

In the second part of the thesis, we investigate the motion planning and control problem in the presence of dynamical uncertainty. In absence of accurate models (e.g., when a manipulator operates while it is in contact with the environment) we should be able to collect data from the interaction to make informed decisions toward task satisfaction. Data-driven approaches are becoming increasingly popular in robotic control under uncertainty and have enabled tackling a wide range of new tasks. However, methods developed to verify safety constraints for a controller are inherently model based. This motivated us to adopt a model-based approach to data-driven control that enables explorative data collection while ensuring system safety. Furthermore, we propose data collection policy alternatives to reduce the well-known distribution shift effect in model learning.