Kavli Affiliate: Wei Gao
| First 5 Authors: Jialong Xue, Wei Gao, Yu Wang, Chao Ji, Dongdong Zhao
| Summary:
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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