From the lab to the desert: Fast prototyping and learning of robot locomotion

Kevin Sebastian Luck, Joseph Campbell, Michael Andrew Jansen, Daniel M. Aukes, Heni Ben Amor

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

We present a methodology for fast prototyping of morphologies and controllers for robot locomotion. Going beyond simulation-based approaches, we argue that the form and function of a robot, as well as their interplay with realworld environmental conditions are critical. Hence, fast design and learning cycles are necessary to adapt robot shape and behavior to their environment. To this end, we present a combination of laminate robot manufacturing and sampleefficient reinforcement learning. We leverage this methodology to conduct an extensive robot learning experiment. Inspired by locomotion in sea turtles, we design a low-cost crawling robot with variable, interchangeable fins. Learning is performed using both bio-inspired and original fin designs in an artificial indoor environment as well as a natural environment in the Arizona desert. The findings of this study show that static policies developed in the laboratory do not translate to effective locomotion strategies in natural environments. In contrast to that, sample-efficient reinforcement learning can help to rapidly accommodate changes in the environment or the robot.
Original languageEnglish
Title of host publicationRobotics: Science and Systems XIII, RSS 2017
EditorsS. Srinivasa, N. Ayanian, N. Amato, S. Kuindersma
PublisherMIT Press Journals
ISBN (Electronic)9780992374730
Publication statusPublished - 2017
Externally publishedYes
Event2017 Robotics: Science and Systems, RSS 2017 - Cambridge, United States
Duration: 12 Jul 201716 Jul 2017

Publication series

NameRobotics: Science and Systems
ISSN (Electronic)2330-765X

Conference

Conference2017 Robotics: Science and Systems, RSS 2017
Country/TerritoryUnited States
CityCambridge
Period12/07/1716/07/17

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