Our system is able to jump and successfully catch the ball at 1.05m high in simulation and 0.8m high in the real world, while the robot is 0.3m high when standing.
Quadrupedal animals have the ability to perform agile while accurate tasks: a trained dog can chase and catch a flying frisbee before it touches the ground; a cat alone at home can jump and grab the door handle accurately. However, agility and precision are usually a trade-off in robotics problems. Recent works in quadruped robots either focus on agile but not-so-accurate tasks, such as locomotion in challenging terrain, or accurate but not-so-fast tasks, such as using an additional manipulator to interact with objects. In this work, we aim at an accurate and agile task, catching a small object hanging above the robot. We mount a passive gripper in front of the robot chassis, so that the robot has to jump and catch the object with extreme precision. Our experiment shows that our system is able to jump and successfully catch the ball at 1.05m high in simulation and 0.8m high in the real world, while the robot is 0.3m high when standing.
We open-sourced our legged robot project, aiming to inspire a series of "xxx DoggBot" creations. Our code and resources are freely available—build upon them to expand this exciting field of robotic locomotion.
This project is supported by Shanghai Qi Zhi Institute and ONR grant N00014-20-1-2675 and has been partially funded by the Shanghai Frontiers Science Center of Human-centered Artificial Intelligence. The experiments of this work were supported by the core facility Platform of Computer Science and Communication, SIST, ShanghaiTech University. Thanks to Qi Wu for the detailed technical support. We thank Zipeng Fu, Xuxin Cheng, Wenhao Yu and Erwin Coumans for the feedback and discussion.
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@inproceedings{duan2024playful,
author = {Duan, Xin and Zhuang, Ziwen and Zhao, Hang and Sören Schwertfeger}
title = {Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion},
booktitle = {arXiv},
year = {2024},
}