High-Precision Transformer-Based Visual Servoing for Humanoid Robots in Aligning Tiny Objects

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.

Authors

• Jialong Xue
• Wei Gao
• Yu Wang
• Chao Ji
• Dongdong Zhao
• Shi Yan
• Shiwu Zhang

Keywords

• Torso
• Visualization
• Head
• Humanoid robots
• Training data
• Fasteners
• Transformer cores
• Transformers
• Cameras
• Visual servoing
• Humanoid Robot
• Tiny Objects
• Transformer
• Target Object
• Joint Angles
• Close Distance
• Convergence Of Error
• Screw Head
• Confidence Interval
• Neural Network
• Kinematic
• High Precision
• Data Collection Process
• Target Location
• Visual Features
• Task Completion
• Feedback Control
• Multilayer Perceptron
• Robotic Arm
• Linear Layer
• Imitation Learning
• Alignment Task
• Distance Loss
• Nonlinear Optimization Method
• Close Range
• Rotation Error
• End-effector Pose
• Output Gain
• L1 Loss