Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots

Andreas Seekircher, Justin Stoecker, Saminda Abeyruwan, Ubbo E Visser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Biped soccer robots have shown drastic improvements in motion skills over the past few years. Still, a lot of work needs to be done with the RoboCup Federation's vision of 2050 in mind. One goal is creating a workflow for quickly generating reliable motions, preferably with inexpensive and accessible hardware. Our hypothesis is that using Microsoft's Kinect sensor in combination with a modern optimization algorithm can achieve this objective. We produced four complex and inherently unstable motions and then applied three contemporary optimization algorithms (CMA-ES, xNES, PSO) to make the motions robust; we performed 900 experiments with these motions on a 3D simulated Nao robot with full physics. In this paper we describe the motion mapping technique, compare the optimization algorithms, and discuss various basis functions and their impact on the learning performance. Our conclusion is that there is a straightforward process to achieve complex and stable motions in a short period of time.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages213-224
Number of pages12
Volume7500 LNAI
DOIs
StatePublished - 2013
Event16th International Symposium on Robot Soccer World Cup, RoboCup 2012 - Mexico City, Mexico
Duration: Jun 18 2012Jun 24 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7500 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other16th International Symposium on Robot Soccer World Cup, RoboCup 2012
CountryMexico
CityMexico City
Period6/18/126/24/12

Fingerprint

Biped Robot
Motion Capture
Optimization Algorithm
Robots
Motion
Particle swarm optimization (PSO)
Physics
Hardware
Sensors
Federation
Period of time
Work Flow
Experiments
Basis Functions
Robot
Unstable
Sensor

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Seekircher, A., Stoecker, J., Abeyruwan, S., & Visser, U. E. (2013). Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7500 LNAI, pp. 213-224). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7500 LNAI). https://doi.org/10.1007/978-3-642-39250-4_20

Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots. / Seekircher, Andreas; Stoecker, Justin; Abeyruwan, Saminda; Visser, Ubbo E.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7500 LNAI 2013. p. 213-224 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7500 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Seekircher, A, Stoecker, J, Abeyruwan, S & Visser, UE 2013, Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7500 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7500 LNAI, pp. 213-224, 16th International Symposium on Robot Soccer World Cup, RoboCup 2012, Mexico City, Mexico, 6/18/12. https://doi.org/10.1007/978-3-642-39250-4_20
Seekircher A, Stoecker J, Abeyruwan S, Visser UE. Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7500 LNAI. 2013. p. 213-224. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-39250-4_20
Seekircher, Andreas ; Stoecker, Justin ; Abeyruwan, Saminda ; Visser, Ubbo E. / Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7500 LNAI 2013. pp. 213-224 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{98ca874993af4d4f8b93786ff0e6f644,
title = "Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots",
abstract = "Biped soccer robots have shown drastic improvements in motion skills over the past few years. Still, a lot of work needs to be done with the RoboCup Federation's vision of 2050 in mind. One goal is creating a workflow for quickly generating reliable motions, preferably with inexpensive and accessible hardware. Our hypothesis is that using Microsoft's Kinect sensor in combination with a modern optimization algorithm can achieve this objective. We produced four complex and inherently unstable motions and then applied three contemporary optimization algorithms (CMA-ES, xNES, PSO) to make the motions robust; we performed 900 experiments with these motions on a 3D simulated Nao robot with full physics. In this paper we describe the motion mapping technique, compare the optimization algorithms, and discuss various basis functions and their impact on the learning performance. Our conclusion is that there is a straightforward process to achieve complex and stable motions in a short period of time.",
author = "Andreas Seekircher and Justin Stoecker and Saminda Abeyruwan and Visser, {Ubbo E}",
year = "2013",
doi = "10.1007/978-3-642-39250-4_20",
language = "English (US)",
isbn = "9783642392498",
volume = "7500 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "213--224",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - Motion capture and contemporary optimization algorithms for robust and stable motions on simulated biped robots

AU - Seekircher, Andreas

AU - Stoecker, Justin

AU - Abeyruwan, Saminda

AU - Visser, Ubbo E

PY - 2013

Y1 - 2013

N2 - Biped soccer robots have shown drastic improvements in motion skills over the past few years. Still, a lot of work needs to be done with the RoboCup Federation's vision of 2050 in mind. One goal is creating a workflow for quickly generating reliable motions, preferably with inexpensive and accessible hardware. Our hypothesis is that using Microsoft's Kinect sensor in combination with a modern optimization algorithm can achieve this objective. We produced four complex and inherently unstable motions and then applied three contemporary optimization algorithms (CMA-ES, xNES, PSO) to make the motions robust; we performed 900 experiments with these motions on a 3D simulated Nao robot with full physics. In this paper we describe the motion mapping technique, compare the optimization algorithms, and discuss various basis functions and their impact on the learning performance. Our conclusion is that there is a straightforward process to achieve complex and stable motions in a short period of time.

AB - Biped soccer robots have shown drastic improvements in motion skills over the past few years. Still, a lot of work needs to be done with the RoboCup Federation's vision of 2050 in mind. One goal is creating a workflow for quickly generating reliable motions, preferably with inexpensive and accessible hardware. Our hypothesis is that using Microsoft's Kinect sensor in combination with a modern optimization algorithm can achieve this objective. We produced four complex and inherently unstable motions and then applied three contemporary optimization algorithms (CMA-ES, xNES, PSO) to make the motions robust; we performed 900 experiments with these motions on a 3D simulated Nao robot with full physics. In this paper we describe the motion mapping technique, compare the optimization algorithms, and discuss various basis functions and their impact on the learning performance. Our conclusion is that there is a straightforward process to achieve complex and stable motions in a short period of time.

UR - http://www.scopus.com/inward/record.url?scp=84883399447&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883399447&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-39250-4_20

DO - 10.1007/978-3-642-39250-4_20

M3 - Conference contribution

SN - 9783642392498

VL - 7500 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 213

EP - 224

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -