深度混合模型:在动态世界的推断和计划
Matteo Priorelli1,2, Ivilin Peev Stoianov1
1Institute of Cognitive Sciences and Technologies, National Research Council of Italy, 35137 Padova, Italy.
Entropy (Basel, Switzerland)
|June 26, 2025
概括
本研究引入了复杂任务规划的积极推断,使用深度混合模型来表示动态决策的身体配置和轨迹. 该方法提供了传统最佳控制方法的替代方案.
科学领域:
- 认知科学 认知科学
- 机器人技术 机器人技术 机器人技术
- 神经科学是一个神经科学.
背景情况:
- 复杂的任务规划通常涉及动态和层次关系.
- 传统的最佳控制依赖于成本功能的优化.
- 一个生物启发的替代框架规划和控制作为一个推断过程称为主动推断.
研究的目的:
- 为复杂的任务规划提出一个主动推理方法.
- 利用离散和连续处理来增强规划能力.
- 扩展计划作为推断研究,并提供最佳控制的替代方案.
主要方法:
- 开发了一个集离散和连续处理的深度混合模型.
- 与对象相比,表示了潜在的身体配置.
- 利用层次关系来实现灵活的体格式扩展和工具使用.
- 定义了用动态元素推断和规划的潜在轨迹.
主要成果:
- 在一个习惯性的任务上对模型进行了评估:在选择移动工具后到达移动物体.
- 证明了模型在各种条件下处理任务的能力.
- 展示了模型的灵活规划和适应能力.
结论:
- 提出的积极推断方法有效地解决复杂,动态的任务.
- 深度混合模型为规划作为推理提供了一个新的框架.
- 这项工作推进了机器人和人工智能的传统最佳控制的替代方向.
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