Hybrid Trajectory Optimization

Hybrid trajectory optimization aims to generate efficient and robust robot motions by combining different optimization techniques, often addressing scenarios with complex contact dynamics. Current research focuses on methods that blend sampling-based approaches (like Model Predictive Path Integral) with gradient-based techniques (such as Interior-Point Differential Dynamic Programming or Sequential Quadratic Programming) to leverage the strengths of both. These advancements are improving the performance of robots in challenging tasks, such as dynamic manipulation, autonomous terrain traversal, and complex locomotion, leading to more efficient and stable robot control in diverse environments.