Implementation of End-to-End Training of Deep Visuomotor Policies for Manipulation of a Robotic Arm of Baxter Research Robot

Kim, Seongun; student, Unist Undergraduate; Kim, Sol A; Lima, Rafael de; Choi, Jaesik

doi:10.7746/jkros.2019.14.1.040

Cited by 3 publications

(1 citation statement)

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“…Deep neural architectures (Fukushima and Miyake, 1980;Hinton et al, 2006) have reached a level comparable to human performance in certain pattern recognition tasks (Krizhevsky et al, 2012). Also in robotic applications, deep networks gain more and more importance, from state abstraction to seamless end-to-end control in complex repetitive tasks (Levine et al, 2016). Moreover, it has been speculated whether deep feed-forward networks can account for some aspects of information processing in the mammalian visual system (Serre et al, 2007), which is not to say that the brain is nothing but a collection of deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

The DIAMOND Model: Deep Recurrent Neural Networks for Self-Organizing Robot Control

Smith

Dharmadi

et al. 2020

Front. Neurorobot.

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The proposed architecture applies the principle of predictive coding and deep learning in a brain-inspired approach to robotic sensorimotor control. It is composed of many layers each of which is a recurrent network. The component networks can be spontaneously active due to the homeokinetic learning rule, a principle that has been studied previously for the purpose of self-organized generation of behavior. We present robotic simulations that illustrate the function of the network and show evidence that deeper networks enable more complex exploratory behavior.

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