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 …

Reinforcement learning (RL) enables robots to learn goal-oriented behavior. In production
processes with high variances, such as joining operations in end-of-line assembly, this is …

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 …

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 …

Precise robotic manipulation skills are desirable in many industrial settings, reinforcement
learning (RL) methods hold the promise of acquiring these skills autonomously. In this …

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 …

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

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

Introduction Robotic assembly tasks require precise manipulation and coordination, often
necessitating advanced learning techniques to achieve efficient and effective performance …

Complex assembly tasks involve nonlinear and low-clearance insertion trajectories with
varying contact forces at different stages. For a robot to solve these tasks, it requires a …