dailyai.report

The GRASP planner lifts trajectories into virtual states to parallelize optimization across time. This approach reduces the computational burden of gradient-based planning in learned dynamics. By adding stochasticity directly to the process, it solves longer-horizon tasks more efficiently. Researchers can now optimize complex agent behaviors without the typical linear time bottleneck.