{"ID":5554345,"CreatedAt":"2026-07-02T02:11:27.934456424Z","UpdatedAt":"2026-07-04T17:38:54.732508317Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01212","arxiv_id":"2607.01212","title":"FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model","abstract":"Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systematic study of real-scale bimanual furniture assembly using Vision-Language-Action models (VLAs). We formalize the task, develop a scalable simulation pipeline for expert data generation and evaluation, and build a VR teleoperation system for single-operator bimanual control to collect high-quality real-world demonstrations. To address extreme long-horizon assembly with up to 7 subtasks and 1550 control steps, we propose a progress-enhanced VLA, finetuned on semantically grounded subtasks, that jointly predicts actions and a continuous progress signal, enabling automatic subtask transitions and reducing compounding errors during inference. We further study perception and control design factors that critically affect precision in real-scale assembly. FurnitureVLA improves average simulation success from 48% to 80% compared to baselines across three furniture types, with an additional 21% gain from our design factor study. We validate on a real Kinova Gen3 platform with only 16% drop on the hardest task.","short_abstract":"Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systematic study of real-scale bimanual furniture assembly using Vision-Language-Action models (VLAs). We formalize the task, develop a scalable simulation pipeline for expert d...","url_abs":"https://arxiv.org/abs/2607.01212","url_pdf":"https://arxiv.org/pdf/2607.01212v1","authors":"[\"Chenyang Ma\",\"Yue Yang\",\"Radu Corcodel\",\"Siddarth Jain\",\"Andrew Wu\",\"Chiori Hori\",\"Diego Romeres\"]","published":"2026-07-01T17:51:21Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[]","has_code":false}