データ合成と統合プランニングによるマニュアル基盤の家電操作のスケーリング
Scaling Manual-Grounded Appliance Manipulation with Data Synthesis and Unified Planning
家電のマニュアルから部品接地・長期プランニング・閉ループ回復データを自動生成するパイプラインMAGEと、それを用いた大規模データセットUseAppliance、およびエンドツーエンドモデルAppliancePlanを提案し、実ロボットで有効性を実証した。
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著者: Yuxing Long, Lei Kang, Ziyan Yu, Yuzheng Gao, Bin Cheng, Jiyao Zhang, Xiaoqi Li, Haolin Yang, Dongjiang Li, Hui Shen, Hao Dong
分類: cs.RO, cs.AI, cs.CL, cs.CV, cs.MM
原文アブストラクト
Operating household appliances requires long-horizon planning that is state-dependent and robust to disturbances, yet existing large models fall short, as no sufficiently diverse, task-oriented dataset exists to support such planning. To bridge this gap, we propose MAGE, a scalable data synthesis pipeline that introduces a novel Hierarchical Appliance Graph (HAG) to automatically generate part grounding, long-horizon planning, and closed-loop recovery data from appliance manuals. With MAGE, we build UseAppliance, the first large-scale dataset for manual-grounded appliance manipulation planning, spanning 22 appliance categories with 89K+ part annotations, 53K+ manipulation tasks, and 33K+ closed-loop adjustment steps. Built on UseAppliance, we develop AppliancePlan, an end-to-end model for manual-grounded appliance manipulation planning. On RealAppliance-Bench, AppliancePlan with only 7B parameters achieves over 10x the best baseline on open-loop planning and consistently outperforms state-of-the-art models across all tasks. Real-robot experiments on six household appliances further confirm effective sim-to-real transfer, marking an important step toward general-purpose household robotics.