114 citations · 145 across the 8 of their papers we have counts for
7 papers · 1 filter
Multi-Stage Cable Routing through Hierarchical Imitation Learning
Jianlan Luo, Charles Xu, Xinyang Geng +5
We study the problem of learning to perform multi-stage robotic manipulation tasks, with applications to cable routing, where the robot must route a cable through a series of clips…
Offline Meta-Reinforcement Learning for Industrial Insertion
Tony Z. Zhao, Jianlan Luo, Oleg Sushkov +5
Reinforcement learning (RL) can in principle let robots automatically adapt to new tasks, but current RL methods require a large number of trials to accomplish this. In this paper,…
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…
Residual Reinforcement Learning for Robot Control
Tobias Johannink, Shikhar Bahl, Ashvin Nair +6
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 equatio…