用扩散模型恢复模仿学习的噪音演示
IEEE transactions on neural networks and learning systems
|September 17, 2025
概括
这项研究引入了一种新的过和恢复框架,以使用杂的专家演示来改进模仿学习 (IL). 该方法有效地过清洁数据并恢复不完美的样本,增强机器人技术的政策学习.
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:
- 模仿学习 (IL) 能够从专家演示中学习政策,而不需要环境相互作用或奖励信号.
- 现有的IL算法通常假定完美的专家数据,这是不现实的,因为人类错误或系统不准确.
- 杂的专家演示对IL的有效政策学习构成重大挑战.
研究的目的:
- 开发一个强大的模仿学习框架,可以有效地处理不完美的专家演示.
- 通过过干净样本和恢复损坏的样本来利用杂的离线演示数据.
- 改进模仿学习在现实世界中与不完善数据的场景中的性能和适用性.
主要方法:
- 提出了一个新的过和恢复框架,以解决模仿学习中的杂专家演示.
- 该框架首先识别并过来自专家演示的清洁数据样本.
- 然后使用条件扩散模型来恢复和恢复噪音或不完美的数据样本.
主要成果:
- 拟议的过和恢复框架在各种领域始终优于现有的方法,包括机器人手臂操纵,灵巧的操纵和移动.
- 废弃性研究证实了框架内单个成分的有效性.
- 该框架在演示数据中证明了对不同类型和不同噪音水平的稳定性.
结论:
- 拟议的框架为在模仿学习中利用杂的线下演示数据提供了实用和有效的解决方案.
- 它通过强有力的处理不完美的专家演示来显著提高模仿学习的性能.
- 这项工作促进了模仿学习在现实世界机器人系统中的应用,因为完美的数据很少.
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