Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
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2026
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| _version_ | 1866908966731120640 |
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| author | Liu, Zhen Ning, Xinyu Hu, Zhe Xie, Xinxin Li, Weize Tang, Zhipeng Wang, Chongyu Yang, Zejun Wang, Hanlin Liu, Yitong Pu, Zhongzhu |
| author_facet | Liu, Zhen Ning, Xinyu Hu, Zhe Xie, Xinxin Li, Weize Tang, Zhipeng Wang, Chongyu Yang, Zejun Wang, Hanlin Liu, Yitong Pu, Zhongzhu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13942 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection Liu, Zhen Ning, Xinyu Hu, Zhe Xie, Xinxin Li, Weize Tang, Zhipeng Wang, Chongyu Yang, Zejun Wang, Hanlin Liu, Yitong Pu, Zhongzhu Robotics 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. |
| title | Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.13942 |