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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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 24; no. 6; pp. 1463 - 1467
Main Authors: Hersch, M., Guenter, F., Calinon, S., Billard, A.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.12.2008
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Description
Summary: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 approach with tools of statistical learning theory and provides a solution to the inverse kinematics problem when dealing with a redundant manipulator. The system is validated on two experiments involving a humanoid robot: putting an object into a box and reaching for and grasping an object.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2008.2006703