mlsanityData to 14 Oct 2026

SimPRIVE: a Simulation framework for Physical Robot Interaction with Virtual Environments

arXiv 2504.21454 ↗First posted 30 Apr 2025cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

The use of machine learning in cyber-physical systems has attracted the interest of both industry and academia. However, no general solution has yet been found against the unpredictable behavior of neural networks and reinforcement learning agents. Nevertheless, the improvements of photo-realistic simulators have paved the way towards extensive testing of complex algorithms in different virtual scenarios, which would be expensive and dangerous to implement in the real world. This paper presents SimPRIVE, a simulation framework for physical robot interaction with virtual environments, which operates as a vehicle-in-the-loop platform, rendering a virtual world while operating the vehicle in the real world. Using SimPRIVE, any physical mobile robot running on ROS 2 can easily be configured to move its digital twin in a virtual world built with the Unreal Engine 5 graphic engine, which can be populated with objects, people, or other vehicles with programmable behavior. SimPRIVE has been designed to accommodate custom or pre-built virtual worlds while being light-weight to contain execution times and allow fast rendering. Its main advantage lies in the possibility of testing complex algorithms on the full software and hardware stack while minimizing the risks and costs of a test campaign. The framework has been validated by testing a reinforcement learning agent trained for obstacle avoidance on an AgileX Scout Mini rover that navigates a virtual office environment where everyday objects and people are placed as obstacles. The physical rover moves with no collision in an indoor limited space, thanks to a LiDAR-based heuristic.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 39 references that are in the map. First posted on arXiv 30 Apr 2025.

Builds on

13 of 39 references.

References · 13

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  3. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  4. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
  5. 2021A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challengescited by 154
  6. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 52
  7. 2022Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspectivecited by 42
  8. 2022Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twincited by 10
  9. 2022CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Modelscited by 8
  10. 2022Towards Next Generation of Pedestrian and Connected Vehicle In-the-loop Research: A Digital Twin Co-Simulation Frameworkcited by 8
  11. 2022Sim2real for Autonomous Vehicle Control using Executable Digital Twincited by 7
  12. 2023Sim-on-Wheels: Physical World in the Loop Simulation for Self-Drivingcited by 7
1 more
  1. 2024A Survey of Neural Network Robustness Assessment in Image Recognitioncited by 3

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  1. 2025Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 Robots
  2. 2024Mixed-Reality Digital Twins: Leveraging the Physical and Virtual Worlds for Hybrid Sim2Real Transition of Multi-Agent Reinforcement Learning Policies
  3. 2026Cooperative-Competitive Team Play of Real-World Craft Robots
  4. 2025SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training
  5. 2023NeuronsGym: A Hybrid Framework and Benchmark for Robot Tasks with Sim2Real Policy Learning
  6. 2025VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotion
  7. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
  8. 2024UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI