arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.16613cs.ROcs.AIcs.NE
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无需大脑:由形态计算驱动的软机器人自适应行为

No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots

University of Vermont(佛蒙特大学)

Alican Mertan, Nick Cheney

AI总结:

本研究提出一种无需显式神经网络大脑、仅依靠软机器人物理体形态计算实现自适应复杂行为的方法,并验证其能模仿逻辑门以构建复杂集体行为。

摘要:

在当代人工智能和机器人学中,将大脑建模为神经网络并利用它来学习智能和自适应行为是非常普遍的做法。虽然这种方法在许多类型的任务中效果很好,但它并不是自然界中存在的唯一智能类型。在本研究中,我们探讨如何在不为机器人控制设置单独且显式大脑的情况下创造智能行为,而是完全将其作为机器人物理体内发生计算的结果。具体而言,我们展示了在基于体素的虚拟软机器人中,通过使用简单的反应性材料来创造自适应和复杂行为,这些材料在不同环境线索下主动改变机器人的形状,从而改变其行为。我们展示了闭环形态计算概念的原理验证,并表明在我们的实现中,它能够实现模仿逻辑门的行为,从而让我们得以演示如何将此类行为组合以构建更复杂的集体行为。

英文摘要:

It is prevalent in contemporary AI and robotics to separately postulate a brain modeled by neural networks and employ it to learn intelligent and adaptive behavior. While this method has worked very well for many types of tasks, it isn't the only type of intelligence that exists in nature. In this work, we study the ways in which intelligent behavior can be created without a separate and explicit brain for robot control, but rather solely as a result of the computation occurring within the physical body of a robot. Specifically, we show that adaptive and complex behavior can be created in voxel-based virtual soft robots by using simple reactive materials that actively change the shape of the robot, and thus its behavior, under different environmental cues. We demonstrate a proof of concept for the idea of closed-loop morphological computation, and show that in our implementation, it enables behavior mimicking logic gates, enabling us to demonstrate how such behaviors may be combined to build up more complex collective behaviors.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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