Pose Retargeting from a Single RGB Camera: Optimization-Based Hand Pose Retargeting and Wrist Pose Estimation

Abstract

Robot teleoperation plays a crucial role in collecting data for large-scale imitation learning. Inferring operator's hand pose is crucial for vision-based teleoperation, and current solutions either rely on additional neural network training or hardware to infer the operator's wrist pose. To our knowledge, there is no open-source, general teleoperation toolkit that can be easily deployed to retarget both hand and wrist poses from a single RGB camera. In this paper, we propose OAT (Optimization-based hAnd pose retargeting and wrisT pose estimation), a streamlined approach to retarget human hand and wrist pose to the robot. We leverage the off-the-shelf MediaPipe framework to estimate the operator's hand pose and employ an optimization-based method to infer the operator's wrist pose within the camera frame by 2D/3D hand joint matching. This integrated pipeline facilitates teleoperation from virtually any location using any device equipped with an RGB camera, offering a highly accessible and easily implementable solution. Furthermore, a hand-based camera calibration optimization is proposed to improve the accuracy of wrist pose estimation. In addition to minimal hardware requirements and deployment convenience, our system also demonstrates superior real-time performance compared to state-of-the-art vision-based teleoperation methods.