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Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped
Carnegie Mellon Univ, Sch Comp Sci, Robot Inst, Pittsburgh, PA 15213 USA..
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, Perception and Learning, RPL.
Carnegie Mellon Univ, Sch Comp Sci, Robot Inst, Pittsburgh, PA 15213 USA..
Carnegie Mellon Univ, Sch Comp Sci, Robot Inst, Pittsburgh, PA 15213 USA..
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2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE, 2018, p. 1771-1778Conference paper, Published paper (Refereed)
Abstract [en]

Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. Simulation can aid in optimizing these controllers if parameters learned in simulation transfer to hardware. Unfortunately, this is often not the case in legged locomotion, necessitating learning directly on hardware. This motivates using data-efficient learning techniques like Bayesian Optimization (BO) to minimize collecting expensive data samples. BO is a black-box data-efficient optimization scheme, though its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge, with a focus on bipedal locomotion. In our previous work, we proposed a feature transformation that projected a 16-dimensional locomotion controller to a 1-dimensional space using knowledge of human walking. When optimizing a human-inspired neuromuscular controller in simulation, this feature transformation enhanced sample efficiency of BO over traditional BO with a Squared Exponential kernel. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot, in both simulation and hardware. We present three different walking controllers and two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.

Place, publisher, year, edition, pages
IEEE, 2018. p. 1771-1778
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-237160DOI: 10.1109/ICRA.2018.8461237ISI: 000446394501055Scopus ID: 2-s2.0-85063147098ISBN: 978-1-5386-3081-5 (print)OAI: oai:DiVA.org:kth-237160DiVA, id: diva2:1258411
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2020-03-05Bibliographically approved

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