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Measuring task complexity of movement skill is an important factor to evaluate a difficulty of learning and/or imitating a task for autonomous robots. Although many complexity-measures are proposed in research areas such as neuroscience, physics, computer science, and biology, there have been little attention on the robotic tasks. To cope with measuring complexity of robotic task, we propose an information-theoretic measure for task complexity of movement skills. By modeling proprioceptive as well as exteroceptive sensor data as multivariate Gaussian distribution, movements of a task can be modeled as probabilistic model. Additionally, complexity of temporal variations is modeled by sampling in time and modeling as individual random variables. To evaluate our proposed complexity measure, several experiments are performed on the real robotic movement tasks.

Abstract-Reinforcement learning (RL) has been used as a learning mechanism for a mobile robot to learn state-action relations without a priori knowledge of working environment. However, most RL methods usually suffer from slow convergence to learn optimum state-action sequence. In this paper, it is intended to improve a learning speed by compounding an existing Q-learning method with a shortest path finding algorithm. To integrate the shortest path algorithm with Qlearning method, a stochastic state-transition model is used to store a previous observed state, a previous action and a current state. Whenever a robot reaches a goal, a Stochastic Shortest Path(SSP) will be found from the stochastic state-transition model. State-action pairs on the SSP will be counted as more significant in the action selection. Using this learning method, the learning speed will be boosted when compared with classical RL methods. To show the validity of our proposed learning technology, several simulations and experimental results will be illustrated.