Deep reinforcement learning (DRL) has achieved great success across multiple fields;
however, in the field of robot control, the acquisition of large amounts of motion data from …

For years, the fully-automated robotic assembly has been a highly sought-after technology in
large-scale manufacturing. Yet it still struggles to find widespread implementation in …

Robotic manipulators are widely used in modern manufacturing processes. However, their
deployment in unstructured environments remains an open problem. To deal with the …

Reinforcement learning (RL) combined with deep neural networks has led to a number of
great achievements for robot control in virtual computer environments, where sufficient data …

Nowadays mobile manipulators are commonly used in assembly tasks, which can reach
greater workspace but also cause more uncertainties. Uncertainty is one of the main factors …

Peg-in-hole assembly with narrow clearance is a typical robotic contact-rich task in industrial
manufacturing. Robot learning allows robots to directly acquire the assembly skills for this …

Closed-chain manipulation occurs when several robot arms perform tasks in cooperation. It
is complex to control a dual-arm system because it requires flexible and adaptable operation …

In this work, motivated by recent manufacturing trends, we investigate autonomous robotic
assembly. Industrial assembly tasks require contact-rich manipulation skills, which are …

Leveraging reinforcement learning on high-precision decision-making in Robot Arm
assembly scenes is a desired goal in the industrial community. However, tasks like Flexible …

Abstract Deep Reinforcement Learning (DRL) is a promising Machine Learning technique
that enables robotic systems to efficiently learn high dimensional control policies. However …