Learning Actionable Representations from Visual Observations

Debidatta Dwibedi
Debidatta Dwibedi
Corey Lynch
Corey Lynch

IROS, 2018.

Cited by: 8|Bibtex|Views104|DOI:https://doi.org/10.1109/IROS.2018.8593951
EI
Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

In this work we explore a new approach for robots to teach themselves about the world simply by observing it. In particular we investigate the effectiveness of learning task-agnostic representations for continuous control tasks. We extend Time-Contrastive Networks (TCN) that learn from visual observations by embedding multiple frames join...More

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