2024
| Powerpoint
| PDF
Optimization is a crucial technology for robotics and provides functionality such as optimal control, motion planning, state estimation, alignment, manipulation, tactile sensing, pose tracking, and safety mechanisms. These solvers are often integrated with learned models that estimate and predict non-trivial parts of the world. *Differentiable optimization* enables the learned model to receive a learning signal from these downstream optimization problems. This signal encourages the model to improve on regions that are important for the optimization problem to work well, rather than making accurate predictions under a supervised loss. This talk will overview the foundations, applications, and recent advancements on these topics, with a focus on continuous optimal control (MPC) and non-linear least squares.
