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Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
May 15, 2017 Β· Declared Dead Β· π 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
"No code URL or promise found in abstract"
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Authors
Fangyi Zhang, JΓΌrgen Leitner, Michael Milford, Peter I. Corke
arXiv ID
1705.05116
Category
cs.RO: Robotics
Cross-listed
cs.AI,
cs.CV,
cs.LG,
eess.SY
Citations
3
Venue
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Last Checked
4 months ago
Abstract
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.
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