如果你关注机器人组装，FurnitureVLA用VLA模型把仿真成功率从48%拉到80%，还能在真机上跑。它处理长程任务的方式很巧妙，值得看看。
FurnitureVLA是首个系统研究真实尺度双手机器人家具组装的工作，使用Vision-Language-Action模型（VLA）。该工作形式化了任务，开发了可扩展仿真管道，并通过VR遥操作收集高质量真实世界演示。模型可处理多达7个子任务和1550控制步骤的极长程组装，通过预测连续进度信号实现自动子任务转换。在三种家具类型上，FurnitureVLA将平均仿真成功率从48%提升到80%，设计因素研究额外带来21%增益。在真实Kinova Gen3平台上，最难任务仅有16%的性能下降。
FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model
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.