Dynamical System Modulation for Robot Learning via Kinesthetic Demonstrations

We present a system for robust robot skill acquisition from kinesthetic demonstrations. This system allows a robot to learn a simple goal-directed gesture and correctly reproduce it despite changes in the initial conditions and perturbations in the environment. It combines a dynamical system control...

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Veröffentlicht in:	IEEE transactions on robotics Jg. 24; H. 6; S. 1463 - 1467
Hauptverfasser:	Hersch, M., Guenter, F., Calinon, S., Billard, A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.12.2008 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Applied sciences Artificial intelligence Computer science; control theory; systems Control systems Control theory. Systems Controllers Dealing Dynamical system control Dynamical systems Dynamics Exact sciences and technology Gaussian mixture regression Grasping Hidden Markov models Humans hybrid joint and end-effector control Initial conditions Intelligent robots Inverse kinematics Kinematics Manipulators Modelling and identification Modulation Robot control Robot programming robot programming by demonstration (PbD) Robot sensing systems Robotic assembly Robotics Robots simple robotic manipulation Spatiotemporal phenomena Gaussian mixture regression Dynamical system control manipulators robot programming by demonstration (PbD) intelligent robots simple robotic manipulation hybrid joint and end-effector control Mixed distribution Gripping Redundancy Intelligent robot Dynamical system Robotics Manipulator Inverse problem Robot programming Humanoid robot Gripper Gesture Probability learning
ISSN:	1552-3098, 1941-0468
Online-Zugang:	Volltext
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