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

arXiv: https://arxiv.org/abs/2210.06887 · HTML: https://mlsanity.com/paper/2210.06887/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Kubric: A scalable dataset generator](https://mlsanity.com/paper/2203.03570/) (2022)
- [Learning Agile Robotic Locomotion Skills by Imitating Animals](https://mlsanity.com/paper/2004.00784/) (2020)
- [Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks](https://mlsanity.com/paper/1812.07252/) (2018)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control](https://mlsanity.com/paper/1909.04947/) (2019)
- [Quantifying the Reality Gap in Robotic Manipulation Tasks](https://mlsanity.com/paper/1811.01484/) (2018)
- [Non-prehensile Planar Manipulation via Trajectory Optimization with Complementarity Constraints](https://mlsanity.com/paper/2109.13145/) (2021)
- [Validating Robotics Simulators on Real-World Impacts](https://mlsanity.com/paper/2110.00541/) (2021)
- [Supervised Autonomous Locomotion and Manipulation for Disaster Response with a Centaur-like Robot](https://mlsanity.com/paper/1809.06802/) (2018)
- [Comparing Alternate Modes of Teleoperation for Constrained Tasks](https://mlsanity.com/paper/1905.04428/) (2019)
- [Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty](https://mlsanity.com/paper/2110.09584/) (2021)

## Built on by (most cited first, 7 of 7)

- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration](https://mlsanity.com/paper/2308.02459/) (2023)
- [SliceIt! -- A Dual Simulator Framework for Learning Robot Food Slicing](https://mlsanity.com/paper/2404.02569/) (2024)
- [Few-Shot Learning of Force-Based Motions From Demonstration Through Pre-training of Haptic Representation](https://mlsanity.com/paper/2309.04640/) (2023)
- [Improving Soft-Capture Phase Success in Space Debris Removal Missions: Leveraging Deep Reinforcement Learning and Tactile Feedback](https://mlsanity.com/paper/2409.12273/) (2024)
- [ContactRL: Safe Reinforcement Learning based Motion Planning for Contact based Human Robot Collaboration](https://mlsanity.com/paper/2512.03707/) (2025)
- [A Comprehensive Review of Generative Physical Artificial Intelligence](https://mlsanity.com/paper/2609.18111/) (2026)

## Abstract (arXiv)

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. Please check our video, source code, and examples included in the supplementary material. Our full code base is open source and can be found at https://github.com/cmower/ros_pybullet_interface.