ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction

Abstract

Reliable contact simulation plays a key role in the development of (semi-)autonomous robots, especially when dealing with contact-rich manipulation scenarios, an active robotics research topic. Besides simulation, components such as sensing, perception, data collection, robot hardware control, human interfaces, etc. are all key enablers towards applying machine learning algorithms or model-based approaches in real world systems. However, there is a lack of software connecting reliable contact simulation with the larger robotics ecosystem (i.e. ROS, Orocos), for a more seamless application of novel approaches, found in the literature, to existing robotic hardware. In this paper, we present the ROS-PyBullet Interface, a framework that provides a bridge between the reliable contact/impact simulator PyBullet and the Robot Operating System (ROS). Furthermore, we provide additional utilities for facilitating Human-Robot Interaction (HRI) in the simulated environment. We also present several use-cases that highlight the capabilities and usefulness of our framework. Our code base is open source and can be found at https://github.com/ros-pybullet/ros_pybullet_interface.

Cite

Text

Mower et al. "ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction." Conference on Robot Learning, 2022.

Markdown

[Mower et al. "ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction." Conference on Robot Learning, 2022.](https://mlanthology.org/corl/2022/mower2022corl-rospybullet/)

BibTeX

@inproceedings{mower2022corl-rospybullet,
  title     = {{ROS-PyBullet Interface: A Framework for Reliable Contact Simulation and Human-Robot Interaction}},
  author    = {Mower, Christopher and Stouraitis, Theodoros and Moura, João and Rauch, Christian and Yan, Lei and Behabadi, Nazanin Zamani and Gienger, Michael and Vercauteren, Tom and Bergeles, Christos and Vijayakumar, Sethu},
  booktitle = {Conference on Robot Learning},
  year      = {2022},
  pages     = {1411-1423},
  volume    = {205},
  url       = {https://mlanthology.org/corl/2022/mower2022corl-rospybullet/}
}