Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Progress Verification

Robotic foundation models have advanced manipulation, yet long-horizon tasks remain challenging under partial observability and uncertain execution. Reliable task completion requires retaining relevant information, verifying intermediate outcomes, and adapting plans to execution feedback. We introduce Goal2Skill, a dual-system framework that couples adaptive planning, proactive memory, and a self-evolving verifier. A high-level VLM constructs and revises sub-task plans, while a low-level VLA executes action chunks. Periodic verification connects these processes by using current observations and task memory to determine when to continue, advance, or trigger reflective correction. Memory preserves information needed by upcoming steps and records previous attempts, supporting targeted revisions while retaining valid progress. The verifier combines lightweight models, rule-based checks, and VLM reasoning, and refines its assessment procedures through execution feedback, with candidate updates validated before adoption. Experiments on RoboMME and RMBench show that Goal2Skill achieves the highest average success rate among non-oracle methods, with particularly strong gains on tasks requiring persistent memory and multi-stage coordination.