Details
| Original language | English |
|---|---|
| Title of host publication | Towards Autonomous Robotic Systems - 12th Annual Conference, TAROS 2011, Proceedings |
| Pages | 231-240 |
| Number of pages | 10 |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 12th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2011 - Sheffield, United Kingdom (UK) Duration: 31 Aug 2011 → 2 Sept 2011 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 6856 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In this paper we investigate and develop a real-world reinforcement learning approach to autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement learning approach, combined with a Gaussian distributed states activation, we are able to teach the robot to navigate towards a docking station, and thus extend the duration of autonomy of the Nao by recharging. The control concept is based on visual information provided by naomarks and six basic actions. It was developed and tested using a real Nao robot within a home environment scenario. No simulation was involved. This approach promises to be a robust way of implementing real-world reinforcement learning, has only few model assumptions and offers faster learning than conventional Q-learning or SARSA.
Keywords
- Autonomous Docking, Humanoid Robots, Nao, Real World, Reinforcement Learning, SARSA
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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Towards Autonomous Robotic Systems - 12th Annual Conference, TAROS 2011, Proceedings. 2011. p. 231-240 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6856 LNAI).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Real-world reinforcement learning for autonomous humanoid robot charging in a home environment
AU - Navarro, Nicolás
AU - Weber, Cornelius
AU - Wermter, Stefan
PY - 2011
Y1 - 2011
N2 - In this paper we investigate and develop a real-world reinforcement learning approach to autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement learning approach, combined with a Gaussian distributed states activation, we are able to teach the robot to navigate towards a docking station, and thus extend the duration of autonomy of the Nao by recharging. The control concept is based on visual information provided by naomarks and six basic actions. It was developed and tested using a real Nao robot within a home environment scenario. No simulation was involved. This approach promises to be a robust way of implementing real-world reinforcement learning, has only few model assumptions and offers faster learning than conventional Q-learning or SARSA.
AB - In this paper we investigate and develop a real-world reinforcement learning approach to autonomously recharge a humanoid Nao robot [1]. Using a supervised reinforcement learning approach, combined with a Gaussian distributed states activation, we are able to teach the robot to navigate towards a docking station, and thus extend the duration of autonomy of the Nao by recharging. The control concept is based on visual information provided by naomarks and six basic actions. It was developed and tested using a real Nao robot within a home environment scenario. No simulation was involved. This approach promises to be a robust way of implementing real-world reinforcement learning, has only few model assumptions and offers faster learning than conventional Q-learning or SARSA.
KW - Autonomous Docking
KW - Humanoid Robots
KW - Nao
KW - Real World
KW - Reinforcement Learning
KW - SARSA
UR - http://www.scopus.com/inward/record.url?scp=80052788667&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23232-9_21
DO - 10.1007/978-3-642-23232-9_21
M3 - Conference contribution
AN - SCOPUS:80052788667
SN - 9783642232312
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 231
EP - 240
BT - Towards Autonomous Robotic Systems - 12th Annual Conference, TAROS 2011, Proceedings
T2 - 12th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2011
Y2 - 31 August 2011 through 2 September 2011
ER -