dailyai.report

The GRASP planner lifts trajectories into virtual states to enable parallel optimization across time. This approach solves the computational bottlenecks typically found in gradient-based planning for learned dynamics. By adding stochasticity directly to the optimization process, it stabilizes long-term predictions. Researchers can now execute complex, multi-step tasks without the usual gradient instability.