Learning inverse kinematics
- Resource Type
- Conference
- Authors
- D'Souza, A.; Vijayakumar, S.; Schaal, S.
- Source
- Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180) Intelligent robots and systems Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on. 1:298-303 vol.1 2001
- Subject
- Robotics and Control Systems
Computing and Processing
Humanoid robots
Manipulators
Spatial resolution
Motion control
Robot kinematics
Constraint optimization
Computer science
Neuroscience
Inverse problems
Statistical learning
- Language
Real-time control of the end-effector of a humanoid robot in external coordinates requires computationally efficient solutions of the inverse kinematics problem. In this context, this paper investigates inverse kinematics learning for resolved motion rate control (RMRC) employing an optimization criterion to resolve kinematic redundancies. Our learning approach is based on the key observations that learning an inverse of a nonuniquely invertible function can be accomplished by augmenting the input representation to the inverse model and by using a spatially localized learning approach. We apply this strategy to inverse kinematics learning and demonstrate how a recently developed statistical learning algorithm, locally weighted projection regression, allows efficient learning of inverse kinematic mappings in an incremental fashion even when input spaces become rather high dimensional. Our results are illustrated with a 30-DOF humanoid robot.