dailyai.report

The BAIR team introduced GRASP, a gradient-based planner that lifts trajectories into virtual states to parallelize optimization across time. This method integrates stochasticity directly into the planning process to avoid local minima. By reducing computational overhead, it allows world models to plan over longer horizons. Practitioners can now optimize complex agent trajectories more efficiently.