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 …

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 …

Uncertain factors in environments restrict the intelligence level of industrial robots. Based on
deep reinforcement learning, a skill-acquisition method is used to solve the posed problems …

Assembly is an essential part of complex product manufacturing. Due to the complexity of
assembly operations and rapidly changing market demands, it is a labor-intensive industry …

Automatic high precision assembly of millimeter sized objects is a challenging task.
Traditional methods for precision assembly rely on explicit programming with real robot …

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 …

Nowadays industrial robots have been increasingly widely deployed across various sectors
and assembly tasks are seen as one of the dominant application fields. The assembly tasks …

The high-rigidity working environment in the industrial insert robotic assembly is likely to
cause damage to the robot and parts, so it is difficult to use traditional position control …

Automatic assembly of elastic components is difficult because of the potential deformation of
parts during the assembly process. Consequently, robots cannot adapt their manipulation to …

An environment cannot be effectively described with a single perception form in skill
learning for robotic assembly. The visual perception may provide the object's apparent …