用Transformer实现人形机器人对微小物体的高精度视觉定位
High-Precision Transformer-Based Visual Servoing for Humanoid Robots in Aligning Tiny Objects
- 融合头部和躯干摄像头与关节角度，基于Transformer进行视觉伺服控制
- 对M4-M8螺丝定位平均误差0.8-1.3毫米，成功率93%-100%
- 适合需要精密操作的人形机器人任务，如装配与维修
真实世界中，人形机器人对微小物体的高精度对齐仍是一个普遍且关键的挑战。本文提出一种基于视觉的框架，用于精确估计并控制手持工具与目标物体之间的相对位置，例如螺丝刀头与螺丝槽。通过融合机器人头部和躯干摄像头的图像及其头部关节角度，所提出的基于Transformer的视觉伺服方法能有效校正近距离下的位置误差。在M4-M8螺丝上的实验表明，平均收敛误差为0.8-1.3毫米，成功率高达93%-100%。对比分析验证了该高精度对齐能力得益于本文提出的距离估计Transformer架构和多感知头机制。
原文摘要 · Abstract (English)
High-precision tiny object alignment remains a common and critical challenge for humanoid robots in real-world. To address this problem, this paper proposes a vision-based framework for precisely estimating and controlling the relative position between a handheld tool and a target object for humanoid robots, e.g., a screwdriver tip and a screw head slot. By fusing images from the head and torso cameras on a robot with its head joint angles, the proposed Transformer-based visual servoing method can correct the handheld tool's positional errors effectively, especially at a close distance. Experiments on M4-M8 screws demonstrate an average convergence error of 0.8-1.3 mm and a success rate of 93\%-100\%. Through comparative analysis, the results validate that this capability of high-precision tiny object alignment is enabled by the Distance Estimation Transformer architecture and the Multi-Perception-Head mechanism proposed in this paper.
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