机器人任务受限优化和适应与概率运动原始的机器人
Guanwen Ding1, Xizhe Zang1, Xuehe Zhang1
1State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.
Biomimetics (Basel, Switzerland)
|December 27, 2024
概括
本研究介绍了一种方法,让机器人从人类演示中学习技能,并使用概率运动原始体 (ProMP) 适应各种任务约束. 这使得制造环境中的机器人操作更加灵活和高效.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 制造业中的机器人需要技能学习和适应以提高效率.
- 运动原始 (MP) 是编码机器人技能的关键.
- 概率运动原始体 (ProMPs) 通过运动分布提供了增强的灵活性.
研究的目的:
- 从有限的人体示范中开发一种学习国会议员的方法.
- 让机器人能够将学习到的技能适应各种任务限制,如路标,关节限制和障碍物.
- 提高机器人的适应性和制造业的效率.
主要方法:
- 从少数人体示范中学习概率运动原体 (ProMPs).
- 使用改进的点通用化来实现平滑的轨迹生成.
- 采用任务受约束优化方法,将约束纳入概率框架中的分析.
- 尽量减少Kullback-Leibler (KL) 差异,使用梯度上升下降进行ProMP优化.
- 开发一个统一的机器人运动适应单个和多个障碍物的方法.
主要成果:
- 从最小的人类投入中成功学习机器人技能.
- 运动的有效适应各种任务限制,包括动态障碍.
- 用编码的ProMPs证明了平滑轨迹的概括性.
- 通过广泛的实验,对7-DOF Xarm机器人进行方法的验证.
结论:
- 拟议的方法使机器人能够从人类演示中有效地学习和适应技能.
- 这种方法提高了机器人在复杂的制造环境中的灵活性和适用性.
- 该框架为任务受限的机器人运动适应提供了强大的解决方案.
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