Based on basic emotion modulation theory and the neural mechanisms of generating complex motor patterns, we introduce a novel emotion-modulated learning rule to train a recurrent neural network, which enables a complex musculoskeletal arm and a robotic arm to perform goal-directed tasks with high accuracy and learning efficiency. Specifically, inspired by the fact that emotions can modulate the process of learning and decision making through neuromodulatory system, we present a model of emotion generation and modulation to adjust the parameters of learning adaptively, including the reward prediction error, the speed of learning, and the randomness in action selection. Additionally, we use Oja learning rule to adjust the recurrent weights in delayed-reinforcement tasks, which outperforms the Hebbian update rule in terms of stability and accuracy. In the experimental section, we use a musculoskeletal model of the human upper limb and a robotic arm to perform goal-directed tasks through trial-and-reward learning, respectively. The results show that emotion-based methods are able to control the arm with higher accuracy and a faster learning rate. Meanwhile, emotional Oja agent is superior to emotional Hebbian one in term of performance.