dailyai.report

GRASP optimizes trajectories by lifting them into virtual states, allowing for parallel optimization across time. This gradient-based planner solves the efficiency bottlenecks typical of learned dynamics. By adding stochasticity directly to the process, it enables more practical long-term planning. Researchers can now execute complex sequences without the usual computational collapse.