GALC:使用Lipschitz约束进行指导扩展学习,以实现稳健的轨迹生成.
IEEE transactions on cybernetics
|March 12, 2026
概括
带有利普希茨约束 (GALC) 的指导放大学习通过生成强大的高回报轨迹来增强线下强化学习 (RL). 这种新的方法可以在复杂的机器人任务中提高政策性能,而不会产生不安全的行为.
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:
- 线下强化学习 (RL) 是有前途的,但依赖于劳动密集型数据收集,特别是对于人形运动.
- 现有的RL数据集数据增强技术往往对噪音敏感,并且在复杂的机器人环境中很难实现.
研究的目的:
- 为线下RL开发一种新的轨迹增强方法,这种方法对噪声不敏感,并提高政策绩效.
- 在复杂的机器人任务中解决当前数据增强方法的局限性.
主要方法:
- 建议使用利普希茨约束 (GALC) 的指导放大学习,这是一种使用奖励放大指导的条件扩散模型的方法.
- 引入了局部Lipschitz连续性约束来调节扩散模型的否定过程,将探索限制在数据集的连续性区域.
- 确保生成的轨迹对噪声不敏感,并防止不安全的行动.
主要成果:
- 盖尔克成功地产生了高回报轨迹,这些轨迹对扰动具有强大耐用性.
- 该方法可以防止产生与环境动态不一致的不安全操作.
- 广泛的实验表明,增强轨迹和政策表现在稀疏奖励和高维度机器人任务方面的显著改善.
结论:
- GALC为线下RL中的数据增强提供了强大而有效的解决方案,特别是在具有挑战性的机器人应用中.
- 提出的利普希茨约束有效地指导了扩散模型,用于生成高质量,安全和对噪声不敏感的轨迹.
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