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信息具身化：信息结构在代码和机器人中的计算作用

Informational Embodiment: Computational role of information structure in codes and robots

ENSEA（法国国立高等电子与应用高等学校）

Alexandre Pitti, Kohei Nakajima, Yasuo Kuniyoshi

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AI总结：

本文提出“信息具身化”框架，将机器人身体视为物理通信通道，利用信息论中的熵最大化原理和Shannon高效编码，研究身体形态如何塑造机器人和代码的信息结构，以优化感知与控制。

摘要：

身体形态在智能体感知和处理信息的方式中扮演着重要角色。我们探讨了信息论（IT）视角，研究传感器的精度、电机的准确性、它们的布局以及身体几何形状如何塑造机器人和计算代码中的信息结构。作为一个原创想法，我们将机器人的身体设想为一个物理通信通道，尽管存在内在噪声和材料限制，信息仍可通过该通道进出传递。据此，熵作为信息和不确定性的度量，可用于最大化机器人设计和算法代码本身的效率。这就是1969年由Barlow在生物学中引入的熵最大化原理（PEM）。Shannon的信源编码定理随后提供了一个框架，用于在感觉运动信息方面比较不同类型的身体。为与PME保持一致，我们引入了IT中使用的一类特殊高效代码，这些代码在信息容量方面达到了Shannon极限，具备纠错、抗噪鲁棒性和节俭性。这些高效代码巧妙地利用了量化和随机性，能够处理不确定性、冗余和紧致性。这些特征可用于智能系统的感知和控制。在各种示例和总结讨论中，我们反思了我们称之为“信息具身化”的框架对运动理论和仿生机器人学的更广泛影响，涉及运动协同、储备池计算和形态计算等概念。这些见解有助于更深入地理解信息论如何与自然和人工系统中智能的具身化相交汇。

英文摘要：

The body morphology plays an important role in the way information is perceived and processed by an agent. We address an information theory (IT) account on how the precision of sensors, the accuracy of motors, their placement, the body geometry, shape the information structure in robots and computational codes. As an original idea, we envision the robot's body as a physical communication channel through which information is conveyed, in and out, despite intrinsic noise and material limitations. Following this, entropy, a measure of information and uncertainty, can be used to maximize the efficiency of robot design and of algorithmic codes per se. This is known as the principle of Entropy Maximization (PEM) introduced in biology by Barlow in 1969. The Shannon's source coding theorem provides then a framework to compare different types of bodies in terms of sensorimotor information. In line with PME, we introduce a special class of efficient codes used in IT that reached the Shannon limits in terms of information capacity for error correction and robustness against noise, and parsimony. These efficient codes, which exploit insightfully quantization and randomness, permit to deal with uncertainty, redundancy and compacity. These features can be used for perception and control in intelligent systems. In various examples and closing discussions, we reflect on the broader implications of our framework that we called Informational Embodiment to motor theory and bio-inspired robotics, touching upon concepts like motor synergies, reservoir computing, and morphological computation. These insights can contribute to a deeper understanding of how information theory intersects with the embodiment of intelligence in both natural and artificial systems.

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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