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

Reinforcement learning for assembly tasks can yield powerful robot control algorithms for
applications that are challenging or even impossible for “conventional” feedback control …

Over the past several years there has been a considerable research investment into
learning-based approaches to industrial assembly, but despite significant progress these …

Robotic assembly is a longstanding challenge, requiring contact-rich interaction and high
precision and accuracy. Many applications also require adaptivity to diverse parts, poses …

Conventional feedback control methods can solve various types of robot control problems
very efficiently by capturing the structure with explicit models, such as rigid body equations …

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

Is it possible to learn policies for robotic assembly that can generalize to new objects? We
explore this idea in the context of the kit assembly task. Since classic methods rely heavily …

Automatic assembly has broad applications in industries. Traditional assembly tasks utilize
predefined trajectories or tuned force control parameters, which make the automatic …

Robotic insertion tasks are characterized by contact and friction mechanics, making them
challenging for conventional feedback control methods due to unmodeled physical effects …

Hierarchical reinforcement learning (HRL) can learn the decomposed subpolicies
corresponding to the local state-space; therefore, it is a promising solution to complex …