The GRASP planner optimizes trajectories by lifting them into virtual states, allowing parallel optimization across time. This method solves the instability common in gradient-based planning for learned dynamics. Researchers at Berkeley AI Research added stochasticity to the process to avoid local minima. The result simplifies complex planning for agents operating over extended time horizons.