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Gerrit Schoettler

2 accepted papers

2020

Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards

IROS 2020poster

Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related physical effects with first-order modeling, traditional control methods often result in brittle and inaccurate controllers,…

Cited by 237SourceScholar
2020

Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks

IROS 2020poster

Robotic insertion tasks are characterized by contact and friction mechanics, making them challenging for conventional feedback control methods due to unmodeled physical effects. Reinforcement learning (RL) is a promising approach for learning control policies in such settings. However, RL can be uns…

Cited by 104SourceScholar