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tiago_dnn_ik

https://github.com/anacg1620/tiago_dnn_ik

Science Score: 39.0%

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    Found 1 DOI reference(s) in README
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    Low similarity (8.9%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: anacg1620
  • Language: Python
  • Default Branch: main
  • Size: 693 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 1 year ago · Last pushed 12 months ago
Metadata Files
Readme Citation

README.md

tiagodnnik

Different methods for learning IK with TIAGo and how to deploy them.

tiagodnnik

This directory contains the necessary code to generate and preprocess training, validation and test datasets, build the models and train them.

tiagodnninference

This directory contains the necessary code to load a pre-trained model, receive a Cartesian target, perform the IK inference and send back the results to the controller.

Deployment

To deploy, launch simulation and tiagodnninference. Both are connected through a ROS topic. If you want to send a target, use:

bash rostopic pub -1 /infer/state_dnn geometry_msgs/Pose "position: x: 0.0 y: 0.0 z: 0.0 orientation: x: 0.0 y: 0.0 z: 0.0 w: 0.0" The inference package will compute the joint space positions and send them to the controller, which will move the arm.

Citation

If you found this project useful, please consider citing the following works:

Calzada-Garcia, A., Victores, J. G., Naranjo-Campos, F. J., & Balaguer, C. (2025). Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results. Applied Sciences, 15(13), 7226.

bibtex @article{calzada-garcia2025ik, author = {Calzada-Garcia, Ana and Victores, Juan G. and Naranjo-Campos, Francisco J. and Balaguer, Carlos}, title = {Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results}, journal = {Applied Sciences}, volume = {15}, year = {2025}, number = {13}, article-number = {7226}, doi = {10.3390/app15137226} }

Owner

  • Login: anacg1620
  • Kind: user

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Dependencies

docker/Dockerfile docker
  • tensorflow/tensorflow 2.14.0-gpu build