阿贾纳:普遍的深度不确定性对于对节的机器人的最小感知
Nitin J Sanket1,2, Chahat Deep Singh1, Cornelia Fermüller1
1Perception and Robotics Group (PRG), University of Maryland, College Park, MD, USA.
Science robotics
|August 16, 2023
概括
这项研究介绍了Ajna,这是一种用于机器人的新型神经网络方法,用于从噪音传感器数据中量化预测不确定性. 这使得在动态环境中可靠的决策和导航成为可能,而无需深度感知.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 机器人在充满噪音的传感器的动态环境中运行,导致不可靠的预测.
- 神经网络擅长进行感知驱动的预测,但往往缺乏不确定性量化.
- 现有的聚变方法需要多次观察,增加计算负载.
研究的目的:
- 在神经网络预测中开发一个数学公式来表达异种类型的 aleatoric 不确定性.
- 引入Ajna网络类,用于实时机器人应用中的高效不确定性估计.
- 为了证明不确定性信息在解决常见的机器人任务中没有深度传感的实用性.
主要方法:
- 制定了一种方法,以获得任意分布的异构偶数定量不确定性.
- 开发了阿贾纳网络,需要最小的计算和轻微的损失函数修改.
- 利用来自光流的不确定性来避免障碍,导航,穿越差距和对象分割.
主要成果:
- 阿贾纳网络可以实时估计资源有限的机器人的不确定性.
- 在躲避障碍物,混乱的导航,空隙飞行和物体堆细分方面表现出与基于深度的方法相当的性能.
- 在使用单眼视觉的四个常见机器人和计算机视觉任务上成功进行了评估.
结论:
- 拟议的阿贾纳网络为机器人提供了一个通用的深度不确定性方法.
- 来自光流的不确定性线索可以有效地替代几项机器人任务的深度信息.
- 这种方法在复杂,动态的环境中提高了机器人的可靠性和自主性.
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