Applying an Extension of Estimation of Distribution Algorithm (EDA) for Mobile Robots to Learn Motion Patterns from Demonstration

This paper proposes a probabilistic evolutionary computing algorithm for robots to learn motion patterns. This algorithm is inspired from Estimation of Distribution Algorithms (EDA). The distribution of chromosomes (not the genes), which have higher fitness values in the configuration space, is estimated in a configuration space. A modified Probabilistic Rapidly growing Random Tree (PRRT)-Connect algorithm is used for searching the configuration space to generate chromosomes which are represented as paths from the starting point to the goal point. Mutation is defined as searching with certain probability outside of the current distribution area (obstacle-free area). This algorithm is applied for robotic learning of motion trajectories through imitation. Simulation and practical experimental results are given in this paper to verify the effectiveness of this algorithm. The major contribution of this paper is proposing an extension of current EDAs, which could be applied for rapid robotic imitation learning.