One of the big challenges in robotics is to endow agents with autonomous and adaptive capabilities. With this purpose, we embedded a cerebellum-based control system into a humanoid robot that becomes capable of handling dynamical external and internal complexity. The cerebellum is the area of the brain that coordinates and predicts the body movements throughout the body-environment interactions. Different biologically plausible cerebellar models are available in literature and have been employed for motor learning and control of simplified objects. We built the canonical cerebellar microcircuit by combining machine learning and computational neuroscience techniques. The control system is composed of the adaptive cerebellar module and a classic control method; their combination allows a fast adaptive learning and robust control of the robotic movements when external disturbances appear. The control structure is built offline, but the dynamic parameters are learned during an online-phase training. The aforementioned adaptive control system has been tested in the Neuro-robotics Platform with the virtual humanoid robot iCub. In the experiment, the robot iCub has to balance with the hand a table with a ball running on it. In contrast with previous attempts of solving this task, the proposed neural controller resulted able to quickly adapt when the internal and external conditions change. Our bio-inspired and flexible control architecture can be applied to different robotic configurations without an excessive tuning of the parameters or customization. The cerebellum-based control system is indeed able to deal with changing dynamics and interactions with the environment. Important insights regarding the relationship between the bio-inspired control system functioning and the complexity of the task to be performed are obtained.

中文翻译：

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仿生控制方法提高了人形机器人在动态环境中行动的适应性。

机器人技术的一大挑战是赋予智能体自主和自适应能力。为此，我们将基于小脑的控制系统嵌入到人形机器人中，使其能够处理动态的外部和内部复杂性。小脑是大脑中负责协调和预测身体与环境相互作用过程中身体运动的区域。文献中提供了不同的生物学上合理的小脑模型，并已用于运动学习和简化物体的控制。我们通过结合机器学习和计算神经科学技术构建了规范的小脑微电路。控制系统由自适应小脑模块和经典控制方法组成；当外部干扰出现时，它们的组合允许快速自适应学习和对机器人运动的鲁棒控制。控制结构是离线构建的，但动态参数是在在线阶段训练期间学习的。上述自适应控制系统已在神经机器人平台上通过虚拟人形机器人 iCub 进行了测试。在实验中，机器人 iCub 必须用手平衡一个有球在上面运行的桌子。与之前解决此任务的尝试相比，所提出的神经控制器能够在内部和外部条件变化时快速适应。我们的仿生灵活控制架构可以应用于不同的机器人配置，而无需过度调整参数或定制。基于小脑的控制系统确实能够处理不断变化的动态以及与环境的相互作用。获得了关于仿生控制系统功能与要执行的任务的复杂性之间的关系的重要见解。