arXiv:2608. 11221v1 Announce Type: new Abstract: Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise.
arXiv:2609.17260v1 Announce Type: new Abstract: Cyber-Physical Systems (CPS) operate through a continuous sense-compute-act loop within an open context environment, making it impossible to anticipate...
The paper reports an empirical study comparing traditional and AI‑enabled Simulink controllers, using a taxonomy of ten structural categories and nine functional roles. Analyzing 62 real‑world models and surveying 13 practitioners, it finds that subsystem organization dominates all controller architectures, AI models rely heavily on discrete dynamics and user‑defined abstraction, and constraint enforcement blocks largely disappear in AI‑enabled designs. These findings highlight architectural tensions and gaps between AI literature and practical implementation.
Our latest robotics techniques allow robot controllers, trained entirely in simulation and deployed on physical robots, to react to unplanned changes in the environment as they solve simple tasks. That is, we’ve used these techniques to build closed-loop systems rather than open-loop ones as before.
arXiv:2606. 18532v1 Announce Type: cross Abstract: AI systems are increasingly evaluated in bounded environments that combine isolation, simulation, instrumentation, supervision, and evidence capture.
arXiv:2504. 09662v4 Announce Type: replace-cross Abstract: Multi-agent large language model simulations have the potential to model complex human behaviors and interactions.
arXiv:2606. 10366v1 Announce Type: cross Abstract: Simulation has become an essential tool for evaluating and improving vision-language-action (VLA) policies, offering scalable, reproducible, and controllable alternatives to costly real-world robot evaluation.
arXiv:2609.24911v1 Announce Type: new Abstract: Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by...
The paper presents a method to estimate inconsistency response surfaces in Cyber‑Physical Systems (CPS) under uncertainty. By reformulating inconsistency as an intervention‑response modeling problem, the authors use Saltelli sampling and multi‑fidelity Monte Carlo estimation to generate datasets, then train a surrogate model that predicts inconsistency from propagated uncertainty geometry. Experiments on 48 scenarios across 10 CPS domains show that the surrogate matches Monte Carlo estimates while dramatically reducing evaluation time, enabling extensive sensitivity analysis and a gradient‑based consistency recourse method to identify minimal interventions that restore consistency.
Social simulation offers the social sciences an experimental instrument that the real world cannot supply, and generative agents have transformed it by acting as silicon samples that unite agent-based...
arXiv:2607. 29405v1 Announce Type: new Abstract: Agentic AI systems act through multi-step trajectories that combine planning, tool use, memory, interaction, and adaptation.
arXiv:2608. 09298v1 Announce Type: cross Abstract: Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation.
We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We’ve used these environments to train models which work on physical robots.