Bootstrapping Virtual Bipedal Walkers with Robotics Scaffolded Learning - Zhu, Jiahui; Rong, Chunyan; Iida, Fumiya; Rosendo, Andre (Frontiers in Robotics and AI, 2021)
We reach walking optimality from a very early age by using natural supports, which can be the hands of our parents, chairs, and training wheels, and bootstrap a new knowledge from the recently acquired one. The idea behind bootstrapping is to use the previously acquired knowledge from simpler tasks to accelerate the learning of more complicated ones. In this paper, we propose a scaffolded learning method from an evolutionary perspective, where a biped creature achieves stable and independent bipedal walking while exploiting the natural scaffold of its changing morphology to create a third limb. The novelty of this work is speeding up the learning process with an artificially recreated scaffolded learning. We compare three conditions of scaffolded learning (free, time-constrained, and performance-based scaffolded learning) to reach bipedalism, and we prove that a performance-based scaffold, which is designed by the walking velocity obtained, is the most conducive to bootstrap the learning of bipedal walking. The scope of this work is not to study bipedal locomotion but to investigate the contribution from scaffolded learning to a faster learning process. Beyond a pedagogical experiment, this work presents a powerful tool to accelerate the learning of complex tasks in the Robotics field.