Search Results for author: Jianlan Luo

Found 6 papers, 1 papers with code

Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study

no code implementations21 Mar 2021 Jianlan Luo, Oleg Sushkov, Rugile Pevceviciute, Wenzhao Lian, Chang Su, Mel Vecerik, Ning Ye, Stefan Schaal, Jon Scholz

In this paper we define criteria for industry-oriented DRL, and perform a thorough comparison according to these criteria of one family of learning approaches, DRL from demonstration, against a professional industrial integrator on the recently established NIST assembly benchmark.

Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data

no code implementations13 May 2020 Mohi Khansari, Daniel Kappler, Jianlan Luo, Jeff Bingham, Mrinal Kalakrishnan

Similar to computer vision problems, such as object detection, Action Image builds on the idea that object features are invariant to translation in image space.

Object Detection Representation Learning

UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands

no code implementations24 Oct 2019 Lin Shao, Fabio Ferreira, Mikael Jorda, Varun Nambiar, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Oussama Khatib, Jeannette Bohg

The majority of previous work has focused on developing grasp methods that generalize over novel object geometry but are specific to a certain robot hand.

Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards

1 code implementation13 Jun 2019 Gerrit Schoettler, Ashvin Nair, Jianlan Luo, Shikhar Bahl, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine

Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction.

Domain Randomization for Active Pose Estimation

no code implementations10 Mar 2019 Xinyi Ren, Jianlan Luo, Eugen Solowjow, Juan Aparicio Ojea, Abhishek Gupta, Aviv Tamar, Pieter Abbeel

In this work, we investigate how to improve the accuracy of domain randomization based pose estimation.

Pose Estimation

Residual Reinforcement Learning for Robot Control

no code implementations7 Dec 2018 Tobias Johannink, Shikhar Bahl, Ashvin Nair, Jianlan Luo, Avinash Kumar, Matthias Loskyll, Juan Aparicio Ojea, Eugen Solowjow, Sergey Levine

In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL.

Cannot find the paper you are looking for? You can Submit a new open access paper.