Bimanual grabbing and tossing of packages onto trays or conveyor belts remains a human activity in the industry. For robots, such a dynamic task requires coordination between two arms and fast adaptation abilities when the tossing target is moving and subject to perturbations. Thus, this paper proposes a control framework that enables a bimanual robotic system to grab and toss objects onto a moving target. We develop a mixed learning-optimization method that computes the tossing parameters necessary to achieve accurate tossing tasks. Hence, we learn an inverse throwing map (a closed-form solution of the inverse non-linear throwing problem) that provides minimum release velocities of the object for given relative release positions. This map is embedded into a kinematics-based bi-level optimization that determines the associated feasible release states (positions and velocities) of the dual-arm robot. Additionally, we propose a closed-form modeling approach of the robot’s tossable workspace (set of all positions reachable by an object if tossed by the robot) and use the model to predict intercept or landing locations that yield high probabilities of task success. Furthermore, we employ dynamical systems to generate the coordinated motion of the dual-arm system and design an adaptation strategy to ensure robustness of the interception in the face of target’s perturbations in speed or location. Finally, we validate experimentally the framework on two 7-DoF robotic arms. We demonstrate the accuracy and robustness of the proposed approach. We also show its speed and energy advantages when compared to the traditional pick-and-place strategy.