In human beings, the natural development of the body has been shown to facilitate learning. This approach has been applied in robotic learning with different results, being an advantage under some conditions and tasks. While it is still not well understood under what conditions morphological development helps to learn, several authors have proposed some high-level notions about when it could be interesting to apply it. In our previous work, we have used these notions with the objective of designing a morphological development strategy that facilitates learning in a bipedal locomotion task with an Artificial Neural Network (ANN) controlled robot. In this paper, we aim to go beyond the qualitative design principles previously used and support such considerations with an empirical quantitative study. An analysis of the learning results and how they are related to the design conditions that were established is carried out based on the evolution of the fitness landscape for each developmental stage. The long-term objective is to develop morphology-agnostic optimization strategies for morphological development, which would reduce the number of samples required and, thus, the computational cost, of learning in ANN-controlled robots.
|Title of host publication||Proceedings of the International Joint Conference on Neural Networks (IJCNN)|
|Publication status||Published - 2022|
|Event||International Joint Conference on Neural Networks - Padova, Italy|
Duration: 18 Jul 2022 → 23 Jul 2022
|Conference||International Joint Conference on Neural Networks|
|Period||18/07/2022 → 23/07/2022|