Modeling of Endpoint Feedback Learning Implemented Through Point-to-Point Learning Control

Shou Han Zhou, Ying Tan, Denny Oetomo, Chris Freeman, Etienne Burdet, Iven Mareels

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


In the last decade, several experiments were conducted to investigate human motor control behavior for the task of arm reaching, using only visual feedback of the final hand position at the end of each reaching motion. Current computational frameworks have yet to model that the humans learn to complete such a task by feedforward action based on the feedback of a displacement error at the end of past reaching motions. This paper demonstrates how such learning can be formulated as an optimization problem. By designing a cost function which weighs the tracking of the target and the smoothness of human motion, the constructed framework, implemented in the form of point-to-point learning control, inherently embeds the feedforward control and enables learning over repeated trials using only the available feedback from past observations, here the endpoint errors of a reaching motion trajectory. The proposed framework is able to reproduce the human learning behavior observed in experiments.

Original languageEnglish
Article number7707419
Pages (from-to)1576-1585
Number of pages10
JournalIEEE Transactions on Control Systems Technology
Issue number5
Publication statusPublished - 1 Sep 2017
Externally publishedYes


  • Computational
  • computational neuroscience
  • human motor control
  • learning control (LC)
  • modeling
  • visuomotor learning

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