| Abstract: |
The development of advanced autonomous robots increasingly depends on simulation platforms that canhandle multi-agent coordination, real-time adaptation, and a smooth transfer of learned behaviors to thereal world. Domains like UAV swarms, industrial automation, and smart agriculture illustrate the urgencyof this need: robotic teams face dynamic conditions requiring continuous reconfiguration, efficient design ex-ploration, and robust performance assurances. Although modern simulators facilitate rapid experimentation,they often fail to fully capture real-world complexities, causing a pronounced “reality gap.” Consequently,there is a demand for more comprehensive frameworks that unify high-fidelity simulation, large-scale designexploration, formal validation, and on-the-fly adaptation.
Platforms like the General Robot Intelligence Development (GRID) framework incorporate modular AI components for robotics, leveraging foundation models for learning-based skill acquisition. While GRID and similar systems allow simulation-based training before real-world deployment, they often lack large-scale batch exploration, rigorous safety validation, and adaptive pipelines that refine systems iteratively. These limitations are especially critical for heterogeneous swarms or safety-sensitive applications.
Three major challenges persist: Scalability, most tools lack integrated support for testing thousands of heterogeneous scenarios. Formal Validation – many platforms provide basic simulations but lack rigorous safety verification against collisions, deadlocks, or resource failures. Robust Sim-to-Real Transfer – unmodeled physics and environmental uncertainties degrade real-world performance, requiring manual code modifications or retraining.
To bridge these gaps, we present IQlForge, an architecture that integrates multi-agent coordination, large-scale simulation-driven design exploration, formal safety verification, and multi-layered adaptation. A corefeature is its multi-layer simulation-to-reality pipeline. IQlForge continuously synchronizes a digital twinwith real-time robot data, detecting discrepancies and refining learned models to minimize the reality gap.Drawing insights from recent UAV swarm research, IQlForge enables high-fidelity simulations of distributedsystems under varying conditions—wind, terrain changes, or dynamic tasks—while formal methods verifythat no unsafe behaviors emerge. Additionally, algorithmic self-optimization and reactive control modulesallow robots to adapt mid-mission, adjusting flight paths, reassigning tasks, or altering parameters if un-expected conditions arise. By combining these elements, IQlForge provides an environment where robotssystematically improve in simulation and arrive in physical tests with validated, context-aware strategies.
IQlForge supports diverse simulations, including UAV swarms for environmental monitoring, vehicular traffic modeling, and agricultural robots optimizing bale delivery. In each case, large-scale exploration identified optimal task allocations, route plans, and adaptation policies that reduced battery consumption, collision risks, and idle time. Formal verification detected failure modes missed by conventional testing, reinforcing safe and efficient deployment. |