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Learning Multiple Models of Non-linear Dynamics for Control Under Varying Contexts
[chapter]
2006
Lecture Notes in Computer Science
For stationary systems, efficient techniques for adaptive motor control exist which learn the system's inverse dynamics online and use this single model for control. However, in realistic domains the system dynamics often change depending on an external unobserved context, for instance the work load of the system or contact conditions with other objects. A solution to context-dependent control is to learn multiple inverse models for different contexts and to infer the current context by
doi:10.1007/11840817_93
fatcat:xxknbi4q6reopnxg6zbz4db6ay