Deep reinforcement learning for high precision assembly tasks
Tadanobu Inoue,Giovanni De Magistris,Asim Munawar,Tsuyoshi Yokoya,Ryuki Tachibana +4 more
- pp 819-825
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TLDR
In this article, a recurrent neural network with reinforcement learning was used to perform a peg-in-hole task with a tight clearance and robustness against positional and angular errors for part-mating.Abstract:
The high precision assembly of mechanical parts requires precision that exceeds that of robots. Conventional part-mating methods used in the current manufacturing require numerous parameters to be tediously tuned before deployment. We show how a robot can successfully perform a peg-in-hole task with a tight clearance through training a recurrent neural network with reinforcement learning. In addition to reducing manual effort, the proposed method also shows a better fitting performance with a tighter clearance and robustness against positional and angular errors for the peg-in-hole task. The neural network learns to take the optimal action by observing the sensors of a robot to estimate the system state. The advantages of our proposed method are validated experimentally on a 7-axis articulated robot arm.read more
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Proceedings ArticleDOI
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards
Gerrit Schoettler,Ashvin Nair,Jianlan Luo,Shikhar Bahl,Juan Aparicio Ojea,Eugen Solowjow,Sergey Levine +6 more
TL;DR: In this paper, the authors consider a variety of difficult industrial insertion tasks with visual inputs and different natural reward specifications, namely sparse rewards and goal images, and show that methods that combine RL with prior information, such as classical controllers or demonstrations, can solve these tasks from a reasonable amount of real-world interaction.
Proceedings ArticleDOI
Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly
Jianlan Luo,Eugen Solowjow,Wen Chengtao,Juan Aparicio Ojea,Alice M. Agogino,Aviv Tamar,Pieter Abbeel +6 more
TL;DR: This paper explicitly considers incorporating operational space force/torque information into reinforcement learning; this is motivated by humans heuristically mapping perceived forces to control actions, which results in completing high-precision tasks in a fairly easy manner.
Proceedings ArticleDOI
Deep Reinforcement Learning for Robotic Assembly of Mixed Deformable and Rigid Objects
TL;DR: Force-torque measurements from a robot arm wrist sensor are integrated two-fold; they are integrated into the policy learning process and they are exploited in an admittance controller that is coupled to the neural network that enables robot learning of contact-rich assembly tasks without explicit joint torque control or passive mechanical compliance.
Proceedings ArticleDOI
A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning
TL;DR: In this paper, the authors show that relatively minor modifications to an off-the-shelf Deep-RL algorithm, combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly.
Journal ArticleDOI
Towards Self-X cognitive manufacturing network: An industrial knowledge graph-based multi-agent reinforcement learning approach
TL;DR: This work stepwise introduces an industrial knowledge graph (IKG)-based multi-agent reinforcement learning (MARL) method for achieving the Self-X cognitive manufacturing network and proposes a graph neural network-based embedding algorithm to achieve semantic-based self-configurable solution searching and task decomposition.
References
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