Optimization algorithms, when they are used in a real-time and safety-critical context, offer the potential for considerably advancing robotic and autonomous systems by improving their ability to initiate, plan, and execute complex missions. To meet distinctly challenging performance and reliability requirements, such automated systems must optimally utilize their full performance envelopes in real-time, while simultaneously satisfying critical mission and environmental constraints. Hence real-time optimization-based control would be a game changing capability, providing solutions to the challenging optimization problems that are ubiquitous in a wide range of applications. Optimization-based problem formulations explicitly consider the problem constraints, objectives, and level of uncertainty involved, so that we can accurately quantify and utilize the system’s performance envelope. These formulations are not only necessary for mathematical completeness and rigor, but also for the accurate quantification of the autonomous systems capabilities. Though optimization provides a powerful formulation framework, there are important challenges in the following areas that must be studied to fully realize this potential: accurate formulations of the control problems as tractable optimization problems, robust real-time implementable numerical optimization methods, and systematic verification for real-time optimization-based control methods and software. To this end, this workshop aims to introduce (i) example applications where real-time optimization can provide significant performance gains or new capabilities; (ii) recent advances in robust real-time optimization algorithms; (iii) methods of verification and certification for real-time algorithms; and (iv) recent demonstrations of real-time optimization based control.