dailyai.report

The GRASP planner optimizes trajectories by lifting them into virtual states, allowing parallelization across time. This approach solves the instability common in gradient-based planning for learned dynamics. Researchers at Berkeley AI Research integrated stochasticity to prevent local minima. The method makes long-term planning practical for complex robotic tasks and simulated environments.