We present a MATLAB toolbox for Automated Reachset Optimal Control (AROC) that automatically synthesizes verified controllers for solving reach-avoid problems using reachability analysis. The toolbox implements two different types of control approaches: When using our verified model predictive controller, a feasible control law is constructed and verified on-the-fly during online application of the system. For motion-primitive-based control, on the other hand, controllers for many motion primitives are synthesized offline and then used for online motion planning with a maneuver automaton. Since our toolbox considers general nonlinear systems with input constraints, state constraints, and bounded disturbances, it is applicable to a very broad class of systems, as we demonstrate with several numerical examples. AROC is available at https://aroc.in.tum.de.

One of the key challenges in dynamic manipulation is to establish continuous contact between a moving object and a manipulator. Direct robotic catching is a benchmark in this context, especially without grasping devices. In this paper, we explain why it is unrewarding to aim for an ideal initial dynamic contact if only imprecise knowledge of the object states is available. Robust initial contacts are proposed such that a successful quasi-direct catch can be predicted. The proposed robustness originates in a negative relative acceleration between object and catching end-effector. Sets, for which an upper negative relative acceleration bound holds, are evaluated throughout an exemplary catching motion. Reachable set computations allow to express and visualize these sets of successful object states at any point in time before contact. The paper closes with a robot-robot experiment that shows successful quasi-direct catches without feedback on the object states for a robotic throw.