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title
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image
site name
author
updated
2026-02-27 06:01:33
raw text
Deep RL for Industrial Insertion Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards Gerrit Schoettler* , Ashvin Nair* , Jianlan Luo , Shikhar Bahl , Juan Aparicio Ojea , Eugen Solowjow , Sergey Levine *Equal Contribution Abstract 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, which have to be manually tuned. Reinforcement learning (RL) methods have been demonstrated to be capable of learning controllers in such environments from autonomous interaction with the environment, but running RL algorithms in the real world poses sample efficiency and safety challenges. Moreover, in practical real-world settings we cannot assume access to perfect state information or dense rew...
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