SimPRIVE: a Simulation framework for Physical Robot Interaction with Virtual Environments (2504.21454v1)

Abstract: 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.

SimPRIVE: A Novel Framework for Physical Robot Interaction with Virtual Environments

The paper presents SimPRIVE, an innovative simulation framework specifically designed for physical robot interaction with virtual environments. SimPRIVE stands out by integrating real-world robotic systems into simulated scenarios using Unreal Engine 5, coupled with ROS 2 communication protocols. This fusion facilitates the creation of a comprehensive testing environment where complex algorithms can be validated without the usual risks associated with real-world implementations.

Simulation Framework

SimPRIVE operates within the vehicle-in-the-loop paradigm, wherein a physical robot moves and interacts synchronously with its digital twin in a virtual environment. The framework leverages Unreal Engine 5 for rendering photorealistic worlds and is configured to communicate with robotics using the ROS 2 platform. The digital twin in SimPRIVE replicates the motion of the physical counterpart, allowing real-time testing and deployment of algorithms, thus providing a safe method to assess AI controllers in potentially hazardous scenarios.

Noteworthy is the framework's capability to generate synthetic sensor data, which algorithms can leverage during testing. These sensor readings include LiDAR and camera data, essential for navigation and object detection tasks. The framework maintains flexibility, accommodating various virtual worlds and robotic systems without heavy computational demand.

Application and Validation

The validation task focused on a reinforcement learning agent trained for obstacle avoidance, tested using the AgileX Scout Mini rover navigating a virtual office setup. Key numerical results highlight the rover's ability to effectively avoid physical collisions in a constrained real-world environment using a LiDAR-enabled safety mechanism. This experiment demonstrates SimPRIVE's capacity to maintain real-world robot control during virtual testing, thereby emphasizing its practical value in cost-effective algorithm validation campaigns.

Implications and Future Directions

SimPRIVE's successful integration of physical robotics with virtual environments provides crucial insights into the practical evaluation of cyber-physical systems. By reducing the inherent risks, expenses, and time commitments of physical testing scenarios, it facilitates a more extensive exploration of algorithmic possibilities within controlled environments. This holds significant implications for advancing AI implementations in robotics, especially in dynamic and unpredictable settings.

Future enhancements may focus on increasing simulation fidelity and expanding compatibility across diverse robotic platforms. Additionally, further research could address bridging the simulation-to-reality gap, particularly in rendering realistic sensory inputs that enhance algorithm training efficiency. The ongoing evolution of mixed reality technologies may offer pathways to enhance simulation realism, offering new opportunities for sophisticated AI training methodologies.

In conclusion, the framework represents a valuable tool for researchers and industry stakeholders, enabling rigorous testing of robotics algorithms while mitigating real-world risks. SimPRIVE's fusion of state-of-the-art graphics and robust robotics protocols sets a promising foundation for future advances in AI-driven cyber-physical systems.