OmniNet: Omnidirectional Jumping Neural Network With Height-Awareness for Quadrupedal Robots

Abstract

In the robotics community, it has been a longstanding challenge for quadrupeds to achieve highly explosive movements similar to their biological counterparts. In this work, we introduce a novel training framework that achieves height-aware and omnidirectional jumping for quadrupedal robots. To facilitate the precise tracking of the user-specified jumping height, our pipeline concurrently trains an estimator that infers the robot and its end-effector states in an online fashion. Besides, a novel reward is involved by solving the analytical inverse kinematics with pre-defined end-effector positions. Guided by this term, the robot is empowered to regulate its gestures during the aerial phase. In the comparative studies, we verify that this controller can not only achieve the longest relative forward jump distance, but also exhibit the most comprehensive jumping capabilities among all the existing jumping controllers. A video summarizing the methodology and the validation in both simulation and real hardware is submitted along with this paper.