Inexact Optimization for Robot Control – INEXACT

Robotic systems are expected to take a large place in tomorrow’s society, far beyond current industrial robots in tightly controlled factory environments, with large impacts in terms of safety, health at work, comfort and productivity.

The motion of robots is typically designed and controlled by specifying numerical objectives and constraints on what they must do, and within which limits. These specifications often conflict, and the actual control must be computed to satisfy all of them in the best possible way. This is naturally achieved by solving a numerical optimization problem.

Such problems are often small enough in robotics that they can be solved exactly in theory, but they are always based on models, and by definition, models reflect reality imperfectly, even more so as we get away from tightly controlled (factory) environments.

We propose a complete change of paradigm, to acknowledge that we actually solve inaccurate optimization problems that provide inaccurate solutions by construction, and explore the following two hypotheses: (H1) We can obtain the exact same performance with imprecise numerical solutions, (H2) we can obtain these imprecise numerical solutions using less costly numerical methods, which can be computed faster, using less demanding hardware.

To the best of our knowledge, these questions have barely been explored and INEXACT will provide the first comprehensive exploration of this topic.

Our short-term ambition is to significantly lower the computational requirements for solving control problems, taking advantage of the imprecisions inherent to robotics control to compute appropriate solutions faster. But ultimately, our long-term ambition is to design less fragile, less expensive and less polluting robots, since being less dependent on precise models can make us less dependent on precise and therefore complex, fragile, expensive and resource-demanding mechatronics.