Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection

• URL: http://arxiv.org/abs/2604.13942v1
• Date: Wed, 15 Apr 2026 14:53:09 GMT
• Title: Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
• Abstract summary: Long-horizon embodied manipulation tasks require persistent memory, adaptive task decomposition, and explicit recovery from execution failures.<n>Our framework explicitly separates high-level semantic reasoning from low-level motor execution.<n>A high-level planner, implemented as a VLM-based agentic module, maintains structured task memory.<n>A low-level executor, instantiated as a VLA-based visuomotor controller, carries out each sub-task.
• Score: 15.30405243180468
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent vision-language-action (VLA) systems have demonstrated strong capabilities in embodied manipulation. However, most existing VLA policies rely on limited observation windows and end-to-end action prediction, which makes them brittle in long-horizon, memory-dependent tasks with partial observability, occlusions, and multi-stage dependencies. Such tasks require not only precise visuomotor control, but also persistent memory, adaptive task decomposition, and explicit recovery from execution failures. To address these limitations, we propose a dual-system framework for long-horizon embodied manipulation. Our framework explicitly separates high-level semantic reasoning from low-level motor execution. A high-level planner, implemented as a VLM-based agentic module, maintains structured task memory and performs goal decomposition, outcome verification, and error-driven correction. A low-level executor, instantiated as a VLA-based visuomotor controller, carries out each sub-task through diffusion-based action generation conditioned on geometry-preserving filtered observations. Together, the two systems form a closed loop between planning and execution, enabling memory-aware reasoning, adaptive replanning, and robust online recovery. Experiments on representative RMBench tasks show that the proposed framework substantially outperforms representative baselines, achieving a 32.4% average success rate compared with 9.8% for the strongest baseline. Ablation studies further confirm the importance of structured memory and closed-loop recovery for long-horizon manipulation.

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