מתכנן פעולה עולמי: רובוטים שמחליטים באופן כללי

תקציר מקורי באנגליתarXiv:2607.27599v2 Announce Type: replace Abstract: Building generalizable robot agents for diverse applications remains a fundamental challenge. While imitation learning-based policies can perform well in familiar training environments, they often struggle to generalize to novel scenes, layouts, and task compositions. To this end, we present World Action Planner, an agentic robot planning system in which the agent searches for and composes executable action plans through imagination with an action-conditioned world model. The search proceeds in a coarse-to-fine manner. First, the agent performs global action optimization by reasoning over imagined world-model rollouts to identify potential failures and refine the proposed action plan. It then performs local action search, comparing the im