动物跑步:为四足机器人学习敏捷的导航
David Hoeller1,2, Nikita Rudin1,2, Dhionis Sako1
1Robotic Systems Lab, ETH Zurich, Zurich, Switzerland.
Science robotics
|March 13, 2024
概括
这项研究介绍了一种完全学习的方法,用于四足机器人的敏捷导航,使他们能够克服类似帕库尔的挑战. 该方法培养了机动技巧和对有效跨越障碍物的层次政策,在现实世界测试中实现高速.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 由于动态运动,机器人环境接触和有限的传感器视图,四足机器人的敏捷导航是复杂的.
- 现有的方法通常需要专家演示,离线计算或先前的环境知识.
研究的目的:
- 开发一种完全学习的方法,用于在四条腿机器人中灵活导航,能够处理复杂的,类似帕库尔的场景.
- 为了使机器人能够自主选择和控制各种障碍物的运动技能.
主要方法:
- 采用了层次化的强化学习框架,培养专业的运动技巧 (行走,跳跃,爬,).
- 制定了一项高级别政策,根据环境和机器人的能力选择和调整技能.
- 一个感知模块被训练来重建障碍物从杂的,封闭的传感器数据的场景理解.
主要成果:
- 提出的方法成功地在没有专家演示或先前的环境知识的情况下完成了具有挑战性的,类似于帕库尔的场景.
- 该系统展示了有效的技能选择和适应,使其能够稳健地穿越障碍物.
- 现实世界的实验表明,成功地从模拟转移到硬件,机器人以每秒2米的速度驾驶障碍物.
结论:
- 这种完全学习的,层次化的方法显著提高了四条腿机器人的敏捷导航能力.
- 该方法为复杂的机器人导航任务提供了强大而适应性的解决方案,克服了以前方法的局限性.
- 成功的现实世界部署验证了该方法的有效性和自主机器人系统的潜力.
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