TY - GEN
T1 - Latent space policy search for robotics
AU - Luck, Kevin Sebastian
AU - Neumann, Gerhard
AU - Berger, Erik
AU - Peters, Jan
AU - Amor, Heni Ben
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/31
Y1 - 2014/10/31
N2 - Learning motor skills for robots is a hard task. In particular, a high number of degrees-of-freedom in the robot can pose serious challenges to existing reinforcement learning methods, since it leads to a high-dimensional search space. However, complex robots are often intrinsically redundant systems and, therefore, can be controlled using a latent manifold of much smaller dimensionality. In this paper, we present a novel policy search method that performs efficient reinforcement learning by uncovering the low-dimensional latent space of actuator redundancies. In contrast to previous attempts at combining reinforcement learning and dimensionality reduction, our approach does not perform dimensionality reduction as a preprocessing step but naturally combines it with policy search. Our evaluations show that the new approach outperforms existing algorithms for learning motor skills with high-dimensional robots.
AB - Learning motor skills for robots is a hard task. In particular, a high number of degrees-of-freedom in the robot can pose serious challenges to existing reinforcement learning methods, since it leads to a high-dimensional search space. However, complex robots are often intrinsically redundant systems and, therefore, can be controlled using a latent manifold of much smaller dimensionality. In this paper, we present a novel policy search method that performs efficient reinforcement learning by uncovering the low-dimensional latent space of actuator redundancies. In contrast to previous attempts at combining reinforcement learning and dimensionality reduction, our approach does not perform dimensionality reduction as a preprocessing step but naturally combines it with policy search. Our evaluations show that the new approach outperforms existing algorithms for learning motor skills with high-dimensional robots.
UR - http://www.scopus.com/inward/record.url?scp=84911500552&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911500552&partnerID=8YFLogxK
U2 - 10.1109/IROS.2014.6942745
DO - 10.1109/IROS.2014.6942745
M3 - Conference contribution
AN - SCOPUS:84911500552
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 1434
EP - 1440
BT - IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
Y2 - 14 September 2014 through 18 September 2014
ER -