iLQR for Piecewise-Smooth Hybrid Dynamical Systems
This work addresses planning challenges for robots in tasks requiring contact changes, but it is incremental as it builds on existing iLQR methods with specific adaptations for hybrid dynamics.
The authors tackled trajectory optimization for robotic systems with changing contact conditions by extending the Iterative Linear Quadratic Regulator (iLQR) method to piecewise-smooth hybrid dynamical systems, using techniques like the saltation matrix and reference extension to handle mode changes and gradient updates.
Trajectory optimization is a popular strategy for planning trajectories for robotic systems. However, many robotic tasks require changing contact conditions, which is difficult due to the hybrid nature of the dynamics. The optimal sequence and timing of these modes are typically not known ahead of time. In this work, we extend the Iterative Linear Quadratic Regulator (iLQR) method to a class of piecewise smooth hybrid dynamical systems by allowing for changing hybrid modes in the forward pass, using the saltation matrix to update the gradient information in the backwards pass, and using a reference extension to account for mode mismatch. We demonstrate these changes on a variety of hybrid systems and compare the different strategies for computing the gradients.