Our brain can be recognized as a network of largely hierarchically organized neural circuits that operate to control specific functions, but when acting in parallel, enable the performance of complex and simultaneous behaviors. Indeed, many of our daily actions require concurrent information processing in sensorimotor, associative, and limbic circuits that are dynamically and hierarchically modulated by sensory information and previous learning. This organization of information processing in biological organisms has served as a major inspiration for artificial intelligence and has helped to create in silico systems capable of matching or even outperforming humans in several specific tasks, including visual recognition and strategy-based games. However, the development of human-like robots that are able to move as quickly as humans and respond flexibly in various situations remains a major challenge and indicates an area where further use of parallel and hierarchical architectures may hold promise. In this article we review several important neural and behavioral mechanisms organizing hierarchical and predictive processing for the acquisition and realization of flexible behavioral control. Then, inspired by the organizational features of brain circuits, we introduce a multi-timescale parallel and hierarchical learning framework for the realization of versatile and agile movement in humanoid robots.

我们的大脑可以被认为是一个由大量分层组织的神经回路组成的网络，这些神经回路用于控制特定的功能，但在并行运行时，可以实现复杂和同时发生的行为。事实上，我们的许多日常行为都需要在感觉运动、联想和边缘回路中同时进行信息处理，这些回路由感觉信息和先前的学习动态和分层调制。这种生物有机体中信息处理的组织是人工智能的主要灵感，并有助于在计算机上创建系统能够在几个特定任务中匹配甚至超越人类，包括视觉识别和基于策略的游戏。然而，能够像人类一样快速移动并在各种情况下灵活响应的类人机器人的开发仍然是一个重大挑战，并表明进一步使用并行和分层架构可能有希望的领域。在本文中，我们回顾了几种重要的神经和行为机制，用于组织分层和预测处理，以获取和实现灵活的行为控制。然后，受大脑回路组织特征的启发，我们引入了一个多时间尺度的并行和分层学习框架，用于在仿人机器人中实现多功能和敏捷的运动。