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一种多剂增强学习方法,用于双脚机器人全方位行走.
Haiming Mou1,2, Jie Xue1,2, Jian Liu2
1School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Biomimetics (Basel, Switzerland)
|December 22, 2023
概括
这项研究引入了一种新的多代理强化学习 (RL) 方法,用于在双脚机器人中实现无的全向行走. 这种方法可以在不同的步态之间实现平稳的过渡,而不会因政策转换而引起的震动.
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:
- 在双脚机器人中通向行走是一个复杂的挑战.
- 现有的强化学习 (RL) 方法通常依赖于状态机器,在步态转换过程中造成不稳定.
- 无的全向步伐和短暂的运动对于先进的双脚机器人至关重要.
研究的目的:
- 开发一种新的多代理RL方法,用于在双脚机器人中无的全向行走.
- 克服基于国家机器的政策切换在现有的RL方法的局限性.
- 为了使不同步态和短暂动作之间的顺过渡.
主要方法:
- 设计了一种多代理RL算法,使用具有政策的演员关键框架进行增强的探索.
- 开发了一个基于欧几里德距离的异质政策经验重复机制.
- 引入定期步行功能和课程学习方法,以提高政策稳定性和更快的融合.
主要成果:
- 提出的方法在单一的政策网络中成功实现了多个步伐.
- 在模拟中展示了不同双脚机器人步态之间的平滑无过渡.
- 验证了多剂RL方法在克服以前的局限性方面的有效性.
结论:
- 新的多代理RL方法为双脚机器人全方位行走提供了强大的解决方案.
- 实现了稳定和平稳的步态过渡,解决了现场的一个关键挑战.
- 这种方法显示了推进双脚机器人运动能力的巨大潜力.
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