CPS: Synergy: Collaborative Research: Mutually Stabilized Correction in Physical Demonstration

CPS: Synergy:Mutually Stabilized Correction in Physical Demonstration; Todd Murphey; Northwestern University How much should a person be allowed to interact with a controlled machine? If that machine is easily destabilized, and if the controller operating it is essential to its operation, the answer may be that the person should not be allowed any control authority at all. How should one algorithmically resolve this tension between the need for stability (something an embedded system should insist upon) and the need for performance (something a person may be much more adept at improving)? Using a combination of techniques coming from machine learning, optimal control, and formal verification, the proposed work focuses on a computable notion of trust that allows the embedded system to assess safety of instruction. For the majority of real-world cyber-physical systems, intuitive procedures for designing controllers are needed. One such approach is to teach a system its control policy via demonstration; however, the control behaviors produced by this data-driven technique are unable to be verified for feasibility or stability. In contrast, sophisticated stability analysis is possible for control behaviors derived via optimal control, including measures of both performance and robustness, but such formulations are rarely intuitive and often require substantial levels of mathematical and software training to implement. The proposed work creates a synergy between intuitive design interfaces for a physical system and the formal verification that control provides. In particular, the proposed approach will derive control behaviors using optimal control while simultaneously engaging a human operator to provide physical guidance for adaptation via corrective demonstration. A fundamental technical challenge lies in the fact that the operator may well destabilize a system that has to operate in the physical world subject to dynamics and sources of uncertainty; moreover, the risk to the system changes from one operator to another. The developed controllers will be verified for stability and robustness, and a formal measure of trust in the operator will be used decide whether to cede control to the operator during physical correction. Hence, how aggressively an operator may run a system will explicitly depend on the system’s assessment of that operator’s past performance. The prototype complex control system is an already-existing mechanical system capable of controlling suspended rigid body systems ranging from a single degree-of-freedom up to hundreds of degrees of freedom. Designing motions for such complex systems using a keyboard and computer screen may be unintuitive, while physically demonstrating or even manipulating the rigid bodies to follow desired trajectories would be much more intuitive but poses the threat of destabilizing the marionette’s