dailyai.report

GRASP lifts trajectories into virtual states to parallelize optimization across time. This gradient-based planner solves the instability issues common in learned dynamics. By adding stochasticity directly to the trajectory, it prevents optimization from collapsing. Researchers at BAIR now provide a scalable path for agents to plan complex tasks over extended time horizons.