Simulator-Agnostic Features

• Simulator-agnostic features are design strategies built on architectural decoupling, modularity, and universal interfaces that enhance simulation independence.
• They employ cross-simulator evaluations and objective functions based on direct physical parameters to ensure consistency and robustness.
• This approach increases portability, scalability, and trustworthiness by mitigating simulator-specific artifacts in research and practical applications.

Simulator-agnostic features are architectural, methodological, and algorithmic properties that enable simulation-based systems, testing frameworks, or learned models to operate independently of the specific characteristics or limitations of any single simulator. These features increase portability, robustness, and generalizability by minimizing sensitivity to simulator-specific artifacts and allowing cross-simulator consistency, enabling more trustworthy scientific experimentation, autonomous system development, and verification.

1. Architectural Decoupling and Modularity

A fundamental strategy for achieving simulator-agnosticism is the decoupling of major system components and enforcing strict modularity in simulator design. In platforms such as Flightmare, the rendering engine (built on Unity) and the physics/dynamics engine are implemented as independent modules communicating asynchronously, with interfaces realized using high-performance asynchronous messaging protocols such as ZeroMQ (Song et al., 2020). This separation permits each module to operate at variable granularities—for example, running high-frequency physics simulation (up to 200,000 Hz) without requiring synchronous photo-realistic rendering, which may function at lower rates (up to 230 Hz).

Similarly, AutoDRIVE Simulator employs a microservice-inspired modular paradigm. Major subsystems—including vehicle dynamics, sensor suites, environment models, communication bridges, and user interfaces—are implemented as distinct, replaceable modules. This organization allows for independent updates or replacements of subsystems (e.g., swapping a vehicle dynamics model or modifying sensor characteristics) without impacting other components (Samak et al., 2022).

The following table summarizes key modularity features as realized in two representative platforms:

Platform	Decoupled Modules	Communication Mechanism
Flightmare	Rendering, Physics	ZeroMQ (async)
AutoDRIVE	Dynamics, Sensors, Bridge	WebSocket

This modularity contributes directly to flexibility, scalability, and the ability to integrate with external software stacks or tools across both research and industry environments.

2. Cross-Simulator and Cross-Stack Evaluation

Simulator-agnostic features are essential when evaluating system performance under different simulation backends or digital twins. For Advanced Driver-Assistance Systems (ADAS) testing, studies have revealed substantial divergence in safety violation and collision statistics when scenarios generated via search-based techniques (such as NSGA-II-based SBST) are executed across different simulators (e.g., TASS/Siemens PreScan versus ESI Pro-SiVIC) (Borg et al., 2020).

Multi-simulation approaches, like MultiSim, introduce an ensemble evaluation methodology in which each generated test input is executed on multiple simulators. Test inputs producing consistent failures across all simulators are prioritized, with average inter-simulator disagreement, denoted as

$$f_d(x) = \frac{\sum_{k=1}^{j} \sum_{l=k+1}^{j} |v_k - v_l|}{\binom{j}{2}},$$

being minimized within the search process (Sorokin et al., 11 Mar 2025). This ensures identification of scenarios that expose failures arising from the system under test, rather than from simulator-specific artifacts, increasing the reliability and generalizability of test outcomes.

3. Simulator-Agnostic Objective Functions and Measures

The robustness of cross-simulator testing is strongly dependent on the definition of objective functions and selection of measures for test success or failure. Empirical results indicate that fitness functions based on directly measurable physical parameters, such as spatial distances

$$\text{FF}_1 = \min(\text{distance}(\text{car}, \text{pedestrian}))$$

and proximity to warning zones

$$\text{FF}_2 = \min(\text{distance}(\text{active warning area}, \text{pedestrian})),$$

exhibit greater consistency across simulators than compound metrics relying on internal sensor or perception models (e.g., Time-To-Collision, FF₃) (Borg et al., 2020). In consequence, the recommendation is to design test objectives and system features using direct, physically meaningful quantities that generalize robustly across simulation environments, minimizing reliance on simulator-specific implementations (such as proprietary sensor models or scene renderings).

4. Universal Interfaces and Interoperability Mechanisms

Interfacing with heterogeneous software stacks and hardware requires communication protocols and integration layers that abstract simulator-internal details. The use of network-based messaging (such as ZeroMQ or WebSockets), robotics middleware (e.g., ROS), and standards for message serialization ensures that perception, planning, or actuation modules can be swapped or relocated with minimal friction (Samak et al., 2022).

AutoDRIVE Simulator exemplifies this through a communication bridge supporting both local and distributed computing; it can run the simulator and control stack on separate machines and provides direct bilateral interfacing with ROS nodes via a dedicated bridge (/autodrive_ros_bridge), transmitting standardized sensor and actuator messages. Support for multiple programming languages (Python, C++) further facilitates integration into existing workflows.

5. Algorithmic Generalization Across Simulator Backends

Algorithmic frameworks with explicit simulator-agnostic design, such as HyPER (Srikishan et al., 13 Mar 2025), employ modular hybrid architectures allowing any simulator (including legacy or non-differentiable systems) to be incorporated into decision pipelines. HyPER’s design comprises a neural surrogate $f_{(\phi)}$, a computational simulator $S$ (requiring only state output, not gradients), and an RL-based decision model $\pi_{(\theta)}$ to mediate between the two during autoregressive rollouts. This fully decoupled interface allows algorithmic pipelines to operate independent of simulator internals, benefiting from both learned surrogates and high-fidelity corrections without additional constraints.

Similarly, in RL, “agnostic policy learning” considers learning the best policy within a class $\Pi$, absent any prior on optimality within the class, and seeks efficiency independently of specific simulator mechanics. The use of advanced environment access (such as resets to seen states or covered statespaces) can, in structured settings like Block MDPs, render agnostic policy learning tractable, leveraging “policy emulator” constructions to build tabular MDPs for class-wide value approximation (Krishnamurthy et al., 7 Apr 2025). This paradigm suggests the possibility of learning and evaluation pipelines that are not coupled to idiosyncratic simulator behaviors.

6. Implications for Verification, Validation, and Trustworthiness

Simulator-agnostic features are vital for the reliable verification and validation (V&V) of autonomous systems and AI/ML-driven controllers. Multi-simulator testing, as advocated in the literature, enables the detection of genuine system flaws and increases confidence in system safety and robustness, especially when physical road or platform testing is impractical (Borg et al., 2020, Sorokin et al., 11 Mar 2025).

Empirical evidence demonstrates that ensemble-based multi-simulator frameworks such as MultiSim identify a significantly higher rate of valid, simulator-agnostic failures (up to 51% more) compared to single-simulator or naïvely combined approaches, supporting a more rigorous and trustworthy engineering process (Sorokin et al., 11 Mar 2025).

7. Performance, Adaptability, and Real-World Applicability

The architectural and algorithmic choices underlying simulator-agnostic features have meaningful performance and adaptability consequences. For example, Flightmare’s decoupling permits optimized resource allocation (200,000 Hz dynamics with 230 Hz rendering when needed), while AutoDRIVE’s universal bridge supports research and prototyping across distinct computing environments (Song et al., 2020, Samak et al., 2022).

HyPER’s adaptive policy dynamically corrects surrogate model drift with minimal simulator calls under changing boundary conditions or noise, directly reducing cumulative error by 47%–78% versus surrogate-only baselines in PDE rollout tasks (Srikishan et al., 13 Mar 2025). Such adaptability—both to task, computational budget, and operational noise—underpins the practical value of simulator-agnostic features in scaling models or controllers from simulation to deployment.

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Simulator-agnostic features represent a cross-disciplinary methodological principle that maximizes transferability and robustness. By decoupling components architecturally, enforcing objective function invariance, leveraging universal interfaces, and supporting ensemble or hybrid algorithmic solutions, researchers and practitioners can ensure that insights and artifacts derived in simulation generalize beyond the confines of any single simulator—ultimately strengthening the evidentiary foundation for real-world application in robotics, autonomous driving, scientific modeling, and reinforcement learning.