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通过同步模拟和物理常识推理来增强机器人感知.
Guillermo Trinidad Barnech1, Gonzalo Tejera1, Juan Valle-Lisboa2
1Facultad de Ingeniería, Universidad de la República, Montevideo 11200, Uruguay.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
将物理模拟器集成到CORTEX认知架构中可以增强机器人的功能. 这种方法简化了复杂的任务,如阻塞处理和活动解释,减少编码需求.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 认知架构是一种认知架构.
背景情况:
- 机器人认知架构需要高效的工作记忆系统.
- 直观的物理原理对于先进的机器人感知和交互至关重要.
- 目前实施复杂机器人功能的方法通常涉及广泛的编码.
研究的目的:
- 为了研究将物理模拟器集成到认知架构中可以简化机器人功能实现的假设.
- 在机器人学中通过物理模拟增强的功能进行分类.
- 为了证明这种整合的经验发现.
主要方法:
- 将CORTEX认知架构与嵌入式物理模拟器 (CoppeliaSim) 结合起来.
- 利用工作记忆的深度状态表示,包括本体学,状态持久性和关系.
- 在现实世界中,使用Kinova Gen3和Open-Manipulator-P机器人手臂进行实验,与模拟器同步.
主要成果:
- 简化了诸如阻塞处理,基于模型的感知,自我校准,场景结构稳定性和人类活动解释等功能.
- 通过将预测模拟结果投射到工作记忆中,证明了整体机器人代理性能的增强.
- 在认知架构中实证物理模拟的好处.
结论:
- 物理模拟器的集成大大简化了先进机器人能力的发展.
- 这种方法为机器人提供了更有效的途径来理解和与复杂的环境互动.
- 嵌入式模拟的CORTEX架构为未来的机器人研发提供了一个强大的框架.
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