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

In the domain of flexible manufacturing, Deep Reinforcement Learning (DRL) has emerged
as a pivotal technology for robotic assembly tasks. Despite advancements in sample …

Reinforcement learning (RL) has been successfully applied to a wealth of robot
manipulation tasks and continuous control problems. However, it is still limited to industrial …

Deep reinforcement learning (RL) has succeeded in robotic manipulation applications.
However, training robots in the real world is challenging due to sample efficiency and safety …

Recently, deep reinforcement learning (RL) has shown some impressive successes in
robotic manipulation applications. However, training robots in the real world is nontrivial …

This paper introduces a discrete-continuous action space to learn insertion primitives for
robotic assembly tasks. Primitives are sequences of elementary actions with certain exit …

Robotic assembly tasks involve complex and low-clearance insertion trajectories with
varying contact forces at different stages. While the nominal motion trajectory can be easily …

As manufacturing demographics change from mass production to mass customization,
advances in human-robot interaction in industries have taken many forms. However, the …

This paper explores the idea that skillful assembly is best represented as dynamic
sequences of Manipulation Primitives, and that such sequences can be automatically …

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 …