Playful DoggyBot：学习敏捷而精确的四足运动
Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion
AI总结：
本文提出基于强化学习的感知—控制解耦系统，使配备被动夹爪的四足机器人在高动态运动中完成追踪和接球，验证了敏捷性与精确交互能力。
摘要：
四足动物在与真实世界物体交互时，能够完成敏捷且富有嬉戏性的任务。例如，训练有素的狗可以在飞盘落地前追踪并接住它，而独自留在家中的猫可能跃起抓住门把手。在高动态运动过程中成功抓取物体，需要高度精确的感知与控制。然而，由于硬件限制，在机器人问题中，敏捷性与精确性通常需要权衡。在本工作中，我们采用一种基于强化学习（Reinforcement Learning，RL）的感知—控制解耦系统，旨在探索四足机器人在高动态运动过程中与物体交互时能够达到的精确程度。实验表明，我们的四足机器人在底盘前方安装了一个被动夹爪，能够完成类似于真正训练有素的狗的追踪和捕捉任务。该机器人可以追踪速度最高达3m/s的空中球体，并且能够跃起并成功接住悬挂在其上方的小物体：在仿真中高度为1.05m，在真实世界中高度为0.8m。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
