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相关实验视频

Updated: May 23, 2025

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
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基于模拟高效训练的机器人操纵器的深度强化学习轨迹规划.

Bin Zhao1,2,3,4, Yao Wu5, Chengdong Wu6,5

  • 1School of Information Science and Engineering, Northeastern University, Shenyang, 110819, China. zhaobin@stumail.neu.edu.cn.

Scientific reports
|March 11, 2025
PubMed
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Applied biochemistry and biotechnology·2014

一个新的多行为体批判性深度决定性政策梯度 (M2ACD) 算法增强了机器人操纵器轨迹规划. 这种方法提高了融合速度和稳定性,优于复杂环境中的现有算法.

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
  • 控制系统 控制系统

背景情况:

  • 机器人操纵器在复杂环境中的轨迹规划仍然具有挑战性.
  • 现有的强化学习方法可能会遭受不稳定性和位置跳跃动.
  • 准确的反向动力学对于精确的机器人运动至关重要.

研究的目的:

  • 为机器人操纵器轨迹规划引入一种新的多行为体批判性深度决定性政策梯度 (M2ACD) 算法.
  • 解决机器人强化学习中高估,偏见和不稳定的问题.
  • 为了提高机器人轨迹在复杂环境中的流性和可靠性.

主要方法:

  • 开发了一种使用牛顿-MP代方法的一般反向动力学算法.
  • 结构化了M2ACD算法,使用双参与者和双关键网络来增强稳定性并减少价值高估.
  • 实施了两阶段奖励 (TSR) 策略,用于操作的接近和关闭阶段.
  • 使用非统一的理性B-Splines (NURBS) 曲线进行轨迹平滑.

主要成果:

  • 与TD3,DARC和DDPG相比,M2ACD算法显示出优异的曲线平滑,收稳定性和收速度.
  • 实验验证证了M2ACD和拟议的动力学算法的有效性.
关键词:
人工智能的应用 人工智能的应用协作机器人是一个协作机器人.深度强化学习的学习.奖励的优先级是奖励.轨道规划 轨道规划 轨道规划

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  • 在轨道规划中,M2ACD算法成功地减轻了位置跳跃动.

    • 结论:

    • M2ACD算法为复杂环境中的机器人操纵器轨迹规划提供了有效的解决方案.
    • 拟议的方法增强了稳定性,收性和轨道平滑性.
    • 这项研究为协作机器人轨迹规划中的先进应用奠定了基础.