Iterative Learning without Reinforcement or Reward for Multijoint Movements: A Revisit of Bernstein's DOF Problem on Dexterity
A robot designed to mimic a human becomes kinematically redundant and its total degrees of freedom becomes larger than the number of physical variables required for describing a given task. Kinematic redundancy may contribute to enhancement of dexterity and versatility but it incurs a problem of ill...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2010/217867 |
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| Summary: | A robot designed to mimic a human becomes kinematically redundant and its total degrees of freedom becomes larger than the number of physical variables required for describing a given task. Kinematic redundancy may contribute to enhancement of dexterity and versatility but it incurs a problem of ill-posedness of inverse kinematics from the task
space to the joint space. This ill-posedness was originally found by Bernstein, who tried to unveil the secret of the
central nervous system and how nicely it coordinates a skeletomotor system with many DOFs interacting in complex ways. In
the history of robotics research, such ill-posedness has not yet been resolved directly but circumvented by introducing
an artificial performance index and determining uniquely an inverse kinematics solution by minimization. This paper tackles
such Bernstein's problem and proposes a new method for resolving the ill-posedness in a natural way without invoking
any artificial index. First, given a curve on a horizontal plane for a redundant robot arm whose endpoint is imposed to trace
the curve, the existence of a unique ideal joint trajectory is proved. Second, such a uniquely determined motion can be
acquired eventually as a joint control signal through iterative learning without reinforcement or reward. |
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| ISSN: | 1687-9600 1687-9619 |