dailyai.report

The GRASP planner optimizes trajectories by lifting them into virtual states, allowing for parallel optimization across time. This approach overcomes the computational bottlenecks of traditional gradient-based planning in learned dynamics. Researchers at BAIR used stochasticity to improve robustness. The method allows agents to plan for longer horizons without exponential cost increases.