Search Results for author: Eugen Solowjow

Found 6 papers, 3 papers with code

Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks

no code implementations29 Apr 2020 Gerrit Schoettler, Ashvin Nair, Juan Aparicio Ojea, Sergey Levine, Eugen Solowjow

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

Meta Reinforcement Learning reinforcement-learning

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

1 code implementation24 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.

reinforcement-learning

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.

reinforcement-learning

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