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 language | English (US) |
|---|---|
| Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Pages | 213-224 |
| Number of pages | 12 |
| Volume | 7500 LNAI |
| DOIs | |
| State | Published - 2013 |
| Event | 16th International Symposium on Robot Soccer World Cup, RoboCup 2012 - Mexico City, Mexico Duration: Jun 18 2012 → Jun 24 2012 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7500 LNAI |
| ISSN (Print) | 03029743 |
| ISSN (Electronic) | 16113349 |
Other
| Other | 16th International Symposium on Robot Soccer World Cup, RoboCup 2012 |
|---|---|
| Country | Mexico |
| City | Mexico City |
| Period | 6/18/12 → 6/24/12 |
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ASJC Scopus subject areas
- Computer Science(all)
- Theoretical Computer Science
Cite this
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 proceeding › Conference contribution
}
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.
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U2 - 10.1007/978-3-642-39250-4_20
DO - 10.1007/978-3-642-39250-4_20
M3 - Conference contribution
AN - SCOPUS:84883399447
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 -