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arXiv · 2409.19920

Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion

Xin Duan·Ziwen Zhuang·Hang Zhao·Soeren Schwertfeger
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Abstract

Quadrupedal animals can perform agile and playful tasks while interacting with real-world objects. For instance, a trained dog can track and catch a flying frisbee before it touches the ground, while a cat left alone at home may leap to grasp the door handle. Successfully grasping an object during high-dynamic locomotion requires highly precise perception and control. However, due to hardware limitations, agility and precision are usually a trade-off in robotics problems. In this work, we employ a perception-control decoupled system based on Reinforcement Learning (RL), aiming to explore the level of precision a quadrupedal robot can achieve while interacting with objects during high-dynamic locomotion. Our experiments show that our quadrupedal robot, mounted with a passive gripper in front of the robot's chassis, can perform both tracking and catching tasks similar to a real trained dog. The robot can follow a mid-air ball moving at speeds of up to 3m/s and it can leap and successfully catch a small object hanging above it at a height of 1.05m in simulation and 0.8m in the real world.

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Xin Duan, Ziwen Zhuang, Hang Zhao, Soeren Schwertfeger. 2025-03-18. Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion. https://arxiv.org/abs/2409.19920

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