变化层次混合物用于反向动力学的概率学学习
IEEE transactions on pattern analysis and machine intelligence
|September 12, 2023
概括
本研究介绍了机器人技术的等级无限局部回归模型. 这些模型有效地处理复杂的数据,为概率回归任务提供更好的性能和规范化.
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
- 概率模型可能模型
背景情况:
- 机器人学中的经典回归模型面临着可扩展性或灵活性限制.
- 在机器人技术中,对计算效率高,规范性良好的概率模型的需求正在增长.
研究的目的:
- 为机器人开发一种新的概率层次模型模型.
- 将内核机器的灵活性与自动机器的可扩展性结合起来.
- 引入具有固有的复杂性规范化的计算高效表示.
主要方法:
- 对局部回归技术的概率解释,近似非线性函数.
- 用贝叶斯非参数来制定具有自适应复杂性的灵活模型.
- 两种高效的变量推理技术用于学习等级无限局部回归模型.
主要成果:
- 在处理非平滑函数和减轻灾难性遗忘方面证明了优势.
- 启用了参数共享并促进了快速预测.
- 在大型逆动态数据集和现实世界的控制场景上进行验证.
结论:
- 层次的无限局部回归模型为复杂的机器人任务提供了强大的解决方案.
- 提出的贝叶斯非参数方法提供了高效,规范化和自适应的概率模型.
- 在现实世界的控制场景中成功应用突出了实际的实用性.
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