ROS Index
Package Summary
| Tags | No category tags. |
| Version | 0.0.0 |
| License | Apache2.0 |
| Build type | CATKIN |
| Use | RECOMMENDED |
Repository Summary
| Description | A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning |
| Checkout URI | https://github.com/opendr-eu/opendr.git |
| VCS Type | git |
| VCS Version | master |
| Last Updated | 2025-01-29 |
| Dev Status | UNKNOWN |
| CI status | No Continuous Integration |
| Released | UNRELEASED |
| Tags | deep-learning robotics |
| Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Package Description
Additional Links
Maintainers
- Daniel Honerkamp
Authors
OpenDR mobile manipulation demo
Live demo of mobile manipulation using the OpenDR toolkit.
Set-up
Follow the ROS-setup described for the mobile_manipulation tool.
Running the example
Mobile manipulation tasks in the analytical environment can be run as follows:
python3 mobile_manipulation_demo.py --env pr2
Available tasks include RndStartRndGoals, RestrictedWs, PickNPlace, Door, Drawer. Specific tasks can be specified by adding the --eval_tasks flag. By default it will evaluate all tasks sequentially.
The robot can be specified by the --env flag to chose between the PR2 and the Tiago. By default this will load a pretrained model.
To run this with controllers in gazebo or the real world, pass in the --eval_worlds flag with values gazebo or real_world. By default it will use the analytical (sim) environment. Note that running on the real robot requires the robot to be set up and to specify goals for the end-effector motions.
For other options, see the arg flags in mobile_manipulation_demo.py.
Acknowledgement
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871449 (OpenDR). This publication reflects the authors’ views only. The European Commission is not responsible for any use that may be made of the information it contains.