Search Results - "Rothörl, Thomas"

A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning by Vecerik, Mel, Sushkov, Oleg, Barker, David, Rothorl, Thomas, Hester, Todd, Scholz, Jon

“…Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based…”
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S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency by Vecerik, Mel, Regli, Jean-Baptiste, Sushkov, Oleg, Barker, David, Pevceviciute, Rugile, Rothörl, Thomas, Schuster, Christopher, Hadsell, Raia, Agapito, Lourdes, Scholz, Jonathan

“…A robot's ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize…”
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A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning by Vecerik, Mel, Sushkov, Oleg, Barker, David, Rothörl, Thomas, Hester, Todd, Scholz, Jon

“…Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based…”
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Sim-to-Real Robot Learning from Pixels with Progressive Nets by Rusu, Andrei A, Vecerik, Mel, Rothörl, Thomas, Heess, Nicolas, Pascanu, Razvan, Hadsell, Raia

“…Applying end-to-end learning to solve complex, interactive, pixel-driven control tasks on a robot is an unsolved problem. Deep Reinforcement Learning…”
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Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards by Vecerik, Mel, Hester, Todd, Scholz, Jonathan, Wang, Fumin, Pietquin, Olivier, Piot, Bilal, Heess, Nicolas, Rothörl, Thomas, Lampe, Thomas, Riedmiller, Martin

“…We propose a general and model-free approach for Reinforcement Learning (RL) on real robotics with sparse rewards. We build upon the Deep Deterministic Policy…”
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Learning Awareness Models by Amos, Brandon, Dinh, Laurent, Cabi, Serkan, Rothörl, Thomas, Colmenarejo, Sergio Gómez, Muldal, Alistair, Erez, Tom, Tassa, Yuval, de Freitas, Nando, Denil, Misha

“…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…”
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