Robotic manipulation within dynamic environments presents challenges to precise control and adaptability. Traditional fixed-view camera systems face challenges adapting to change viewpoints and scale variations, limiting perception and manipulation precision. To tackle these issues, we propose the Active Vision-driven Robotic (AVR) framework, a teleoperation hardware solution that supports dynamic viewpoint and dynamic focal length adjustments to continuously center targets and maintain optimal scale, accompanied by a corresponding algorithm that effectively enhances the success rates of various operational tasks. Using the RoboTwin platform with a real-time image processing plugin, AVR framework improves task success rates by 5%-16% on five manipulation tasks. Physical deployment on a dual-arm system demonstrates in collaborative tasks and 36% precision in screwdriver insertion, outperforming baselines by over 25%. Experimental results confirm that AVR framework enhances environmental perception, manipulation repeatability (40% 1 cm error), and robustness in complex scenarios, paving the way for future robotic precision manipulation methods in the pursuit of human-level robot dexterity and precision.
View on arXiv@article{liu2025_2503.01439,
title={ AVR: Active Vision-Driven Robotic Precision Manipulation with Viewpoint and Focal Length Optimization },
author={ Yushan Liu and Shilong Mu and Xintao Chao and Zizhen Li and Yao Mu and Tianxing Chen and Shoujie Li and Chuqiao Lyu and Xiao-ping Zhang and Wenbo Ding },
journal={arXiv preprint arXiv:2503.01439},
year={ 2025 }
}