Deep reinforcement learning for high precision assembly tasks
By Tadanobu Inoue, Giovanni De Magistris, Asim Munawar, Tsuyoshi Yokoya, Ryuki Tachibana. IEEE/RJS International Conference on Intelligent RObots and Systems, 2017
300 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/6f0e3d8784bfa55e6769bf747e33ed4b7000846e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Karol Hausman
- Mel Vecerík
- Jonathan Scholz
- Todd Hester
- Alexander Toshev
- David Barker
- Sören Pirk
In Control and Systems Engineering
- Karol Hausman
- Todd Hester
- Alexander Toshev
- David Barker
- Sören Pirk
- Oleg O. Sushkov
- Mohi Khansari
- Jonathan Scholz
Citations in the corpus
- A Practical Approach to Insertion with Variable Socket Position Using Deep Reinforcement Learning (2018, background): “[9] also computes the reward from the robot kinematics, which restricts its applicability for deformable objects.”
- Modeling Long-horizon Tasks as Sequential Interaction Landscapes (2020, background): “Combined with deep neural networks, this has shown to be a promising direction to learn object assembly [21] or more advanced motion models [3].”