Hierarchical Learning for Modular RobotsDownload PDF

12 Feb 2018 (modified: 05 May 2023)ICLR 2018 Workshop SubmissionReaders: Everyone
Abstract: We argue that hierarchical methods can become the key for modular robots achieving reconfigurability. We present a hierarchical approach for modular robots that allows a robot to simultaneously learn multiple tasks. Our evaluation results present an environment composed of two different modular robot configurations, namely 3 degrees-of-freedom (DoF) and 4DoF with two corresponding targets. During the training, we switch between configurations and targets aiming to evaluate the possibility of training a neural network that is able to select appropriate motor primitives and robot configuration to achieve the target. The trained neural network is then transferred and executed on a real robot with 3DoF and 4DoF configurations. We demonstrate how this technique generalizes to robots with different configurations and tasks.
Keywords: modular robots, reconfigurable robots, Hierarchical RL, Deep RL, multi-task neural networks
TL;DR: Hierarchical RL methods for modular reconfigurable robots transferred from simulation to real robot.
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