20 citations · 23 across the 3 of their papers we have counts for
7 papers
Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study
Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute +6
Over the past several years there has been a considerable research investment into learning-based approaches to industrial assembly, but despite significant progress these techniqu…
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
Mohi Khansari, Daniel Kappler, Jianlan Luo +2
This paper introduces Action Image, a new grasp proposal representation that allows learning an end-to-end deep-grasping policy. Our model achieves grasp success on re…
UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands
Lin Shao, Fabio Ferreira, Mikael Jorda +6
To achieve a successful grasp, gripper attributes such as its geometry and kinematics play a role as important as the object geometry. The majority of previous work has focused on…
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
Gerrit Schoettler, Ashvin Nair, Jianlan Luo +4
Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related phy…
Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly
Jianlan Luo, Eugen Solowjow, Chengtao Wen +4
Precise robotic manipulation skills are desirable in many industrial settings, reinforcement learning (RL) methods hold the promise of acquiring these skills autonomously. In this…
Domain Randomization for Active Pose Estimation
Xinyi Ren, Jianlan Luo, Eugen Solowjow +4
Accurate state estimation is a fundamental component of robotic control. In robotic manipulation tasks, as is our focus in this work, state estimation is essential for identifying…