Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination

被引：1

作者：

Zhang, Fangyi ^{[1
]}

Leitner, Jurgen ^{[1
]}

Milford, Michael ^{[1
]}

Corke, Peter I. ^{[1
]}

机构：

[1] QUT, ACRV, Brisbane, Qld, Australia

来源：

2017 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW) | 2017年

基金：

澳大利亚研究理事会;

关键词：

D O I：

10.1109/CVPRW.2017.74

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently. Benefiting from weighted losses, the fine-tuning method significantly improves the performance of the policies for a robotic planar reaching task.

引用

页码：496 / 497

页数：2

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