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Learning the peg-into-hole assembly operation with a connectionist reinforcement technique

  • Published:1997-08 Issue:1 Volume:33 Page:101-109
  • ISSN:0166-3615
  • Container-title:Computers in Industry
  • language:en
  • Short-container-title:Computers in Industry

Author:

Nuttin M.,Van Brussel H.

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference17 articles.

1. Teaching and learning of compliance using neural nets: representation and generation of nonlinear compliance;Asada,1990

2. Connectionist reactive control for robotic assembly tasks by soft sensored grippers;Borovac,1993

3. Compliant robot motion I, a formalism for specifying compliant motion tasks;De Schutter;International Journal of Robotics Research,1988

4. Compliant robot motion II, a control approach based on external control loops;De Schutter;International Journal of Robotics Research,1988

5. An empirical study of learning speed in back propagation networks;Fahlman,1988

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1. High Precision Peg-in-Hole Assembly Approach Based on Sensitive Robotics and Deep Recurrent Q-Learning;Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022;2023

2. Trajectory Control of An Articulated Robot Based on Direct Reinforcement Learning;Robotics;2022-10-20

3. Deep Deterministic Policy Gradient with Reward Function Based on Fuzzy Logic for Robotic Peg-in-Hole Assembly Tasks;Applied Sciences;2022-03-21

4. Learning-based Contact Status Recognition for Peg-in-Hole Assembly;2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS);2021-09-27

5. Learning Stable Normalizing-Flow Control for Robotic Manipulation;2021 IEEE International Conference on Robotics and Automation (ICRA);2021-05-30
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