Adapting to uncertainties is essential yet challenging for robots while conducting assembly tasks in real‐world scenarios. Reinforcement learning (RL) methods provide a promising solution for these cases. However, training robots with RL can be a data‐extensive, time‐consuming, and potentially unsafe process. In contrast, classical control strategies can have near‐optimal performance without training and be certifiably safe. However, this is achieved at the cost of assuming that the environment is known up to small uncertainties. Herein, an architecture aiming at getting the best out of the two worlds, by combining RL and classical strategies so that each one deals with the right portion of the assembly problem, is proposed. A time‐varying weighted sum combines a recurrent RL method with a nominal strategy. The output serves as the reference for a task space impedance controller. The proposed approach can learn to insert an object in a frame within a few minutes of real‐world training. A success rate of 94% in the presence of considerable uncertainties is observed. Furthermore, the approach is robust to changes in the experimental setup and task, even when no retrain is performed. For example, the same policy achieves a success rate of 85% when the object properties change.

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This Supporting information includes interactive plots, videos, and data captured while performing evaluation and validation experiments for our paper.

The process of repairing damaged area or reconstructing specific area or removing unwanted objects from video is known as video inpainting. Most of the automatic techniques are available to deal with this problem but most of them are unable to repair large holes. To get sharp inpainted area, in this paper we have proposed an efficient algorithm using exemplar-based technique. Here, considered static camera which gives video having stationery background with moving foreground. To detect the region of moving objects we apply edge detection technique. Once object region detected priority assignment to the patches is applied. A natural image has structures and textures. Structure sparsity was measure to find similarities of the patches. The patch having higher sparseness is then selected and its priority is set which is the highest priority among the patches. This patch is then used for further inpainting.

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