四足机器人学会边跑边接飞球，像狗一样敏捷精准。
Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion
- 用强化学习解耦感知与控制，提升动态抓取精度
- 仿真中可接3米/秒空中球，实机跳1.05米高抓物
- 适合研究复杂环境下的机器人动态抓取任务
四足动物可在真实世界中完成敏捷且灵活的动作，例如受过训练的狗能在飞盘落地前将其接住，或独居猫咪会跃起抓门把手。在高速运动中成功抓取物体需要极高的感知与控制精度。然而，由于硬件限制，机器人通常在敏捷性与精确性之间难以兼顾。本文提出一种基于强化学习的感知-控制解耦系统，旨在探索四足机器人在高速运动中与物体交互时能达到的精度水平。实验表明，搭载前置被动夹爪的四足机器人能实现类似真实训练犬的追踪与接物行为：在仿真中可跟踪速度高达3米/秒的空中球，并跳跃抓取悬挂在1.05米高度的小物体；在真实环境中，亦可成功抓取0.8米高度的目标。
原文摘要 · Abstract (English)
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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