Real-time Holistic Robot Pose Estimation with Unknown States

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles, which are not always available in real-world scenarios. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work addresses the urgent need for efficient robot pose estimation with unknown states. We propose an end-to-end pipeline for real-time, holistic robot pose estimation from a single RGB image, even in the absence of known robot states. Our method decomposes the problem into estimating camera-to-robot rotation, robot state parameters, keypoint locations, and root depth. We further design a corresponding neural network module for each task. This approach allows for learning multi-facet representations and facilitates sim-to-real transfer through self-supervised learning. Notably, our method achieves inference with a single feedforward, eliminating the need for costly test-time iterative optimization. As a result, it delivers a 12-time speed boost with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code is available at https://oliverbansk.github.io/Holistic-Robot-Pose/.