Godon Jean-Merwan, Argentieri Sylvain, Gas Bruno
Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, ISIR, Paris, France.
Front Robot AI. 2020 Dec 1;7:561660. doi: 10.3389/frobt.2020.561660. eCollection 2020.
For naive robots to become truly autonomous, they need a means of developing their perceptive capabilities instead of relying on hand crafted models. The sensorimotor contingency theory asserts that such a way resides in learning invariants of the sensorimotor flow. We propose a formal framework inspired by this theory for the description of sensorimotor experiences of a naive agent, extending previous related works. We then use said formalism to conduct a theoretical study where we isolate sufficient conditions for the determination of a sensory prediction function. Furthermore, we also show that algebraic structure found in this prediction can be taken as a proxy for structure on the motor displacements, allowing for the discovery of the combinatorial structure of said displacements. Both these claims are further illustrated in simulations where a toy naive agent determines the sensory predictions of its spatial displacements from its uninterpreted sensory flow, which it then uses to infer the combinatorics of said displacements.
对于原始机器人要真正实现自主，它们需要一种发展其感知能力的方法，而不是依赖手工制作的模型。感觉运动偶联理论断言，这样一种方式存在于学习感觉运动流的不变量中。我们提出了一个受该理论启发的形式框架，用于描述原始智能体的感觉运动体验，扩展了先前的相关工作。然后，我们使用所述形式主义进行理论研究，在其中我们分离出确定感觉预测函数的充分条件。此外，我们还表明，在该预测中发现的代数结构可以用作运动位移结构的代理，从而能够发现所述位移的组合结构。在模拟中进一步说明了这两个主张，其中一个玩具原始智能体从其未解释的感觉流中确定其空间位移的感觉预测，然后它使用该预测来推断所述位移的组合学。
