基于双代理DDPG方法的运动规划框架,用于由人类关节角度限制指导的双臂机器人
Keyao Liang1, Fusheng Zha1, Wei Guo1
1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.
Frontiers in neurorobotics
|March 8, 2024
概括
本研究介绍了一种用于双臂机器人的新型运动规划框架,使用人类关节角度约束来使学习变得人性化,并加速复杂任务的轨迹规划. 该方法提高了培训效率和控制.
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
- 运动规划 运动规划
背景情况:
- 强化学习 (RL) 在机器人运动规划中很普遍.
- 现有的RL方法在复杂的双臂机器人多步骤任务中面临挑战,包括大型探索空间,延长训练时间和有限的过程控制.
- 这些局限性阻碍了机器人的高效和类似人类的行为.
研究的目的:
- 为双臂机器人开发一个改进的运动规划框架.
- 为了解决当前基于RL的轨迹规划方法的局限性.
- 实现人性化的学习内容和风格,同时实现复杂的多步骤任务的快速,协调的轨迹规划.
主要方法:
- 该框架使用双代理深确定性策略梯度 (DADDPG) 算法.
- 它结合了从人类手臂运动数据 (IMU) 和人机器人运动映射模型中得出的人类关节角度约束.
- 以这些约束为指导的细分奖励功能旨在减少探索空间并加速训练.
主要成果:
- 该框架通过使用Baxter机器人在达到-抓取-调整任务模拟中进行了验证.
- 结果表明,人类经验知识显著影响学习指导.
- 拟议的方法可以更快地规划协调双臂轨迹的多步任务.
结论:
- 开发的运动规划框架有效地将人类关节角度限制纳入RL.
- 这种方法提高了复杂任务的双臂机器人运动计划的效率和可控性.
- 这些发现突出了人类指导的RL在更直观,更快速的机器人学习方面的潜力.
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