dailyai.report

The BAIR team introduced GRASP, a gradient-based planner that optimizes trajectories by lifting them into virtual states. This architecture allows parallel optimization across time and integrates stochasticity directly into the process. It solves the vanishing gradient problem common in long-horizon tasks. Practitioners can now plan more complex sequences in learned dynamics without computational collapse.