基于内在动机的智能机器人自主发育算法

针对两轮自平衡机器人在学习过程中主动性差的问题,受心理学内在动机理论启发,提出一种基于内在动机的智能机器人自主发育算法。该算法在强化学习的理论框架中,引入模拟人类好奇心的内在动机理论作为内部驱动力,与外部奖赏信号一起作用于整个学习过程。采用双层内部回归神经网络存储知识的学习与积累,使机器人逐步学会自主平衡技能。最后针对测量噪声污染对机器人平衡控制中两轮角速度的影响,进一步采用卡尔曼滤波方法进行补偿,以提高算法收敛速度,降低系统误差。仿真实验表明,该算法能够使两轮机器人通过与环境的交互获得认知,成功地学会运动平衡控制技能。

Autonomous developmental algorithm for intelligent robot based on intrinsic motivation

The initiative of two-wheeled self-balancing robot in the process of learning is poor. Inspired by intrinsic motivation theory of psychology, an autonomous development algorithm for intelligent robot based on intrinsic motivation was put forward. In the frame work of the reinforcement learning theory, the algorithm introduced human curiosity of intrinsic motivation theory as the internal driving force, and external reward signal into entire learning progress, and adopted double internal regression neural network for storage of knowledge learning and accumulation, which made robot gradually learn autonomous balance skill. Finally, aiming at the effects of measurement noise pollution on two-wheeled angular velocity of robot, further by adopting the method of Kalman filter to compensate, to speed up the algorithm convergence, and reduce the system error. Simulation experiments show that this algorithm can make the two-wheeled robot obtain cognition through interaction with the environment, therefore successfully learn balance control skill.