Evaluation of Deep Reinforcement Learning Methods for Modular Robots

2018/02/07 by Risto Kojcev, Kojcev, Risto, Nora Etxezarreta +5
Computer Science · Engineering · #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotics (cs.RO) #cs.RO

paper · pdf · doi:10.48550/arxiv.1802.02395

arxiv created 2018/02/07 · openalex publication_date 2018/02/07 · arxiv updated 2018/02/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We propose a novel framework for Deep Reinforcement Learning (DRL) in modular robotics using traditional robotic tools that extend state-of-the-art DRL implementations and provide an end-to-end approach which trains a robot directly from joint states. Moreover, we present a novel technique to transfer these DLR methods into the real robot, aiming to close the simulation-reality gap. We demonstrate the robustness of the performance of state-of-the-art DRL methods for continuous action spaces in modular robots, with an empirical study both in simulation and in the real robot where we also evaluate how accelerating the simulation time affects the robot's performance. Our results show that extending the modular robot from 3 degrees-of-freedom (DoF), to 4 DoF, does not affect the robot's learning. This paves the way towards training modular robots using DRL techniques.

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