2020/03/12 by M. Naya-Varela, A. Faina, Naya-Varela, M. +4 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotic Locomotion and Control #Robotics (cs.RO) #cs.AI #cs.RO
paper · pdf · doi:10.48550/arxiv.2003.07195
10 pages, 4 figures. arXiv admin note: text overlap with arXiv:2003.05817
arxiv created 2020/03/12 · openalex publication_date 2020/03/12 · arxiv updated 2020/03/17 · openalex created_date 2020/03/23 · openalex updated_date 2026/07/28
Morphological development is part of the way any human or animal learns. The learning processes starts with the morphology at birth and progresses through changing morphologies until adulthood is reached. Biologically, this seems to facilitate learning and make it more robust. However, when this approach is transferred to robotic systems, the results found in the literature are inconsistent: morphological development does not provide a learning advantage in every case. In fact, it can lead to poorer results than when learning with a fixed morphology. In this paper we analyze some of the issues involved by means of a simple, but very informative experiment in quadruped walking. From the results obtained an initial series of insights on when and under what conditions to apply morphological development for learning are presented.