在行动选择过程中避免障碍的综合计算方法
Shan Zhong1,2, Nader Pouratian3, Paul Schrater4
1Alfred E.Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
本研究提出了一个计算模型,用于在混乱的环境中选择动作. 该模型整合了目标追求和避开障碍,成功模拟了复杂环境中的人类运动行为.
科学领域:
- 计算神经科学是一种计算神经科学.
- 发动机控制器 发动机控制器
- 认知机器人技术是如何实现的
背景情况:
- 在混乱的环境中选择动作是一个复杂的认知和运动挑战.
- 现有的模型往往难以将目标导向的行为与避开障碍的行为结合起来.
- 了解大脑如何平衡接近和避免对于自然主义运动至关重要.
研究的目的:
- 为行动选择开发一个集成目标方法和避开障碍的计算模型.
- 将随机最佳控制理论扩展到一个新的障碍回避框架.
- 为动态的政策整合提供统一的"相对可取性"衡量标准.
主要方法:
- 提出了一个扩展随机最佳控制理论的计算模型.
- 将行动选择分解为方法和规避控制政策的加权组合.
- 将目标,障碍和行动中的综合价值信息整合到一个统一的可取性衡量标准中.
- 在混乱的环境中使用模拟的目标实现任务来评估框架.
主要成果:
- 该模型成功地捕获了模拟混乱环境中人类运动行为的关键特征.
- 证明了障碍物特性对运动轨迹的影响.
- 观察了模型在避免障碍之前复制向障碍物移动的短暂倾向的能力.
结论:
- 拟议的框架为理解复杂的自然环境中的行动选择提供了一种新的方法.
- 提供了关于接近和避免行为之间的动态相互作用的见解.
- 该模型作为研究大脑面临的行动选择挑战的综合框架.
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