Search Results for author: Thomas Rothörl

Found 5 papers, 1 papers with code

S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency

no code implementations30 Sep 2020 Mel Vecerik, Jean-Baptiste Regli, Oleg Sushkov, David Barker, Rugile Pevceviciute, Thomas Rothörl, Christopher Schuster, Raia Hadsell, Lourdes Agapito, Jonathan Scholz

In this work we advocate semantic 3D keypoints as a visual representation, and present a semi-supervised training objective that can allow instance or category-level keypoints to be trained to 1-5 millimeter-accuracy with minimal supervision.

Image Reconstruction Representation Learning

A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning

no code implementations2 Oct 2018 Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jon Scholz

Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing.

Robotics

Learning Awareness Models

no code implementations ICLR 2018 Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal, Tom Erez, Yuval Tassa, Nando de Freitas, Misha Denil

We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world.

Sim-to-Real Robot Learning from Pixels with Progressive Nets

no code implementations13 Oct 2016 Andrei A. Rusu, Mel Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell

The progressive net approach is a general framework that enables reuse of everything from low-level visual features to high-level policies for transfer to new tasks, enabling a compositional, yet simple, approach to building complex skills.

reinforcement-learning

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