TY - GEN
T1 - Statistical dynamical systems for skills acquisition in humanoids
AU - Calinon, Sylvain
AU - Li, Zhibin
AU - Alizadeh, Tohid
AU - Tsagarakis, Nikos G.
AU - Caldwell, Darwin G.
PY - 2012
Y1 - 2012
N2 - Learning by imitation in humanoids is challenging due to the unpredictable environments these robots have to face during reproduction. Two sets of tools are relevant for this purpose: 1) probabilistic machine learning methods that can extract and exploit the regularities and important features of the task; and 2) dynamical systems that can cope with perturbation in real-time without having to replan the whole movement. We present a learning by imitation approach combining the two benefits. It is based on a superposition of virtual spring-damper systems to drive a humanoid robot's movement. The method relies on a statistical description of the springs attractor points acting in different candidate frames of reference. It extends dynamic movement primitives models by formulating the dynamical systems parameters estimation problem as a Gaussian mixture regression problem with projection in different coordinate systems. The robot exploits local variability information extracted from multiple demonstrations of movements to determine which frames are relevant for the task, and how the movement should be modulated with respect to these frames. The approach is tested on the new prototype of the COMAN compliant humanoid with time-based and time-invariant movements, including bimanual coordination skills.
AB - Learning by imitation in humanoids is challenging due to the unpredictable environments these robots have to face during reproduction. Two sets of tools are relevant for this purpose: 1) probabilistic machine learning methods that can extract and exploit the regularities and important features of the task; and 2) dynamical systems that can cope with perturbation in real-time without having to replan the whole movement. We present a learning by imitation approach combining the two benefits. It is based on a superposition of virtual spring-damper systems to drive a humanoid robot's movement. The method relies on a statistical description of the springs attractor points acting in different candidate frames of reference. It extends dynamic movement primitives models by formulating the dynamical systems parameters estimation problem as a Gaussian mixture regression problem with projection in different coordinate systems. The robot exploits local variability information extracted from multiple demonstrations of movements to determine which frames are relevant for the task, and how the movement should be modulated with respect to these frames. The approach is tested on the new prototype of the COMAN compliant humanoid with time-based and time-invariant movements, including bimanual coordination skills.
UR - http://www.scopus.com/inward/record.url?scp=84891139169&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84891139169&partnerID=8YFLogxK
U2 - 10.1109/HUMANOIDS.2012.6651539
DO - 10.1109/HUMANOIDS.2012.6651539
M3 - Conference contribution
AN - SCOPUS:84891139169
SN - 9781467313698
T3 - IEEE-RAS International Conference on Humanoid Robots
SP - 323
EP - 329
BT - 2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012
T2 - 2012 12th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2012
Y2 - 29 November 2012 through 1 December 2012
ER -