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Thomas Rothörl

4 accepted papers

2020

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

Mel Vecerik, Jean-Baptiste Regli, Oleg Sushkov, David Barker, Rugile Pevceviciute, Thomas Rothörl +3
CoRL 2020

A robot’s ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize general-purpose training criteria, e.g. image reconstruction, smoothness in latent space, or usefulness for control, or else make use of large datasets an

Cited by 0Source ↗PDF ↗Scholar ↗
2019

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

Mel Vecerik, Oleg Sushkov, David Barker, Thomas Rothörl, Todd Hester, Jon Scholz
ICRA 2019poster

Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each in…

Cited by 136Source ↗Scholar ↗
2018

Learning Awareness Models

Brandon Amos, Laurent Dinh, Serkan Cabi, Thomas Rothörl, Sergio Gómez Colmenarejo, Alistair Muldal +4
ICLR 2018poster

We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally ava…

Cited by 58Source ↗PDF ↗Scholar ↗
2017

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

Andrei A. Rusu, Matej Večerík, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, Raia Hadsell
CoRL 2017

Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning algorithms are too slow to achieve performance on a real robot, but their potential has been demonstrated in simulated environments. We propose using

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