通过示范扩展与执行设计改进细粒度操作
Improving Fine-Grained Manipulation through Demonstration Expansion and Execution Design
Jingxu Gu, Dongze Lian
AI 速览：DEED 通过结构化示范扩展与执行设计，将预训练视觉-语言-动作策略适配到五个 MetaFine 细粒度操作任务，作者报告官方 Overall 得分为 85.55%。
Fine-grained manipulation requires a robot policy to follow instructions, remain robust to visual variations, and translate predicted actions into precise robot control. We present Demonstration Expansion and Execution Design (DEED), a framework for adapting a pretrained vision-language-action policy to the five MetaFine tasks through structured demonstration expansion and execution design. DEED generates demonstrations for different instructions from the same initial scene and renders each physical trajectory under different camera positions and lighting conditions while preserving its original states and action targets. A hierarchical sampling strategy balances instructions, trajectories, and visual conditions during supervised adaptation. At deployment, DEED converts predicted action chunks into robot commands using inverse kinematics, gripper mappings selected for each task, and closed-loop replanning. DEED achieves an official Overall score of 85.55%. Fixed-policy evaluations show that performance varies with execution horizon, denoising budget, and gripper mapping.