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SBRN
1998
IEEE

Competitive and Temporal Hebbian Learning for Production of Robot Trajectories

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Competitive and Temporal Hebbian Learning for Production of Robot Trajectories
This paper proposes an unsupervised neural algorithm for trajectory production of a 6-DOF robotic arm. The model encodes these trajectories in a single training iteration by using competitive and temporal Hebbian learning rules and operates by producing the current and the next position for the robotic arm. In this paper we will focus on trajectories with one common point. These types of trajectories introduce some ambiguities, but even so, the neural algorithm is able to reproduce them accurately and unambiguously due to context units used as part of the input. In addition, the proposed model is shown to be fault-tolerant.
Guilherme De A. Barreto, Aluizio F. R. Araú
Added 05 Aug 2010
Updated 05 Aug 2010
Type Conference
Year 1998
Where SBRN
Authors Guilherme De A. Barreto, Aluizio F. R. Araújo
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