Peers’ Experience Learning for Developmental Robots

Abstract

Humans have a fundamental ability that is to learn others' experience for their own use, while humanoid robots don't have. Several attempts have been made for specific situations in evolution and study of developmental robots. However, such attempts have provided limitations, e.g. others' experience learning get overlooked. The present article proposes peers' experience learning method, which first reviewed the evolution and development of developmental robots as some typical studies revealed, moving from humanlike to developmental. These terms are then reconsidered from humanoid robots' viewpoint, particularly with the developmental principles: the verification principle and the embodiment principle. Next, a conceptual model of peers' experience learning is proposed based on the principles, and the simulation results show that robots can "copy" peers' experience to cognize and develop automatically. Finally, a general discussion and proposals for addressing future issues are given.

Cognitive Control Architecture for the Practical Realization of UAV Collision Avoidance

“…One of the key hallmark requirements of unmanned aerial vehicles (UAVs) is the ability to avoid obstacles autonomously [1][2][3][4]. Many scholars have studied automatic collision avoidance, with the aim of pushing forward the development of UAVs.…”