dailyai.report

The GRASP planner optimizes trajectories by lifting them into virtual states, allowing for parallelization across time. This approach solves the instability typically found in gradient-based planning for learned dynamics. It enables agents to plan over longer horizons without the usual computational bottlenecks. Researchers can now apply these optimizations to complex, stochastic environments.