dailyai.report

The GRASP planner optimizes trajectories by lifting them into virtual states, allowing parallelization across time. This approach reduces the computational cost of gradient-based planning in learned dynamics. Researchers at BAIR used this method to solve complex tasks over longer horizons. It provides a more efficient alternative to traditional sequential optimization.