基于深度增强学习的稳定跳跃控制,用于灵感来自虫的机器人
Qijie Zhou1,2, Gangyang Li1,2, Rui Tang1,2
1Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Biomimetics (Basel, Switzerland)
|September 27, 2024
概括
本研究介绍了一种稳定的跳跃控制算法,用于使用深度强化学习的虫灵感机器人. 这种新的方法提高了跳跃的准确性和稳定性,使能耗高效的机动.
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
- 生物机械工程 生物机械工程
背景情况:
- 生物启发的跳跃机器人提供了先进的移动性,但在跳跃时难以保持姿势稳定.
- 快速的姿势变化大大降低了机器人跳跃运动的准确性和稳定性.
研究的目的:
- 开发一个稳定的跳跃控制算法,用于使用深度强化学习的虫灵感机器人.
- 为了提高腿类机器人跳跃运动的稳定性和准确性.
主要方法:
- 采用了与演员和批评者网络的深度强化学习框架.
- 该算法将机器人的观测结果直接映射到联合扭矩,并将探索的率术语纳入.
- 设计了一个带有阶段激励机制的动态奖励功能,以提高稳定性和准确性.
主要成果:
- 这种以虫为灵感的机器人表现出平滑,非翻转的跳跃,目标距离准确度的误差不到3%.
- 跳跃机车与步行相同的距离相比,消耗的能量减少了44.6%.
- 拟议的算法显示了比经典方法更快的融合和更好的性能.
结论:
- 深度强化学习为稳定和准确的机器人跳跃提供了一种有效的方法.
- 开发的算法显著提高了跳跃机器人的能效和运动能力.
- 这项研究为更灵活,更有效的生物灵感机器人铺平了道路,这些机器人能够跨越复杂的地形.
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