  Pose estimation commonly refers to computer vision methods that recognize
people's body postures in images or videos. With recent advancements in deep
learning, we now have compelling models to tackle the problem in real-time.
Since these models are usually designed for human images, one needs to adapt
existing models to work on other creatures, including robots. This paper
examines different state-of-the-art pose estimation models and proposes a
lightweight model that can work in real-time on humanoid robots in the RoboCup
Humanoid League environment. Additionally, we present a novel dataset called
the HumanoidRobotPose dataset. The results of this work have the potential to
enable many advanced behaviors for soccer-playing robots.
