Robot learning schemes that trade motion accuracy for command simplification

Kuu-Young Young, Jyh Fu Lee, Hui Jun Jou

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This study was inspired by the human motor control system in its ability to accommodate a wide variety of motions. By contrast, the biologically inspired robot learning controller usually encounters huge learning space problems in many practical applications. A hypothesis for the superiority of the human motor control system is that it may have simplified the motion command at the expense of motion accuracy. This tradeoff provides an insight into how fast and simple control can be achieved when a robot task does not demand high accuracy. Two motion command simplification schemes are proposed in this paper based on the equilibrium-point hypothesis for human motion control. Investigation into the tradeoff between motion accuracy and command simplification reported in this paper was conducted using robot manipulators to generate signatures. Signature generation involves fast handwriting, and handwriting is a human skill acquired via practice. Because humans learn how to sign their names after they learn how to write, in the second learning process, they somehow learn to trade motion accuracy for motion speed and command simplicity, since signatures are simplified forms of original handwriting. Experiments are reported that demonstrate the effectiveness of the proposed schemes.

Original languageEnglish
Pages (from-to)313-329
Number of pages17
JournalFuzzy Sets and Systems
Issue number3
StatePublished - 16 Mar 2000


  • Robotics
  • Learning control
  • Command simplification
  • Human control
  • Fuzzy neural network


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