| 2017 data-sort-value="2024-01-01" | 2024 || [https://arxiv.org/abs/17032402.06907 Sim05741 Real-to-Real Transfer world Robot Applications of Robotic Control with Dynamics RandomizationFoundation Models: A Review] || Josh Tobin K Kawaharazuka, T Matsushima et al. || simulated data can be used to train robotic control policies that transfer well to overview of the practical application of foundation models in real -world using dynamics randomizationrobotics, bridging including the gap between simulation and real-world dataintegration of specific components within existing robot systems.

| 2017 data-sort-value="2024-01-02" | 2024 || [https://arxiv.org/abs/16122405.07828 Learning from Simulated 03520 Is SORA a World Simulator? A Comprehensive Survey on General World Models and Unsupervised Images through Adversarial TrainingBeyond] || Ashish Shrivastava Z Zhu, X Wang, W Zhao, C Min, N Deng, M Dou et al. || technique that refines simulated images to make them more realistic using adversarial trainingsurveys the applications of world models in various fields, including robotics, enhancing and discusses the quality potential of synthetic data for training robotics modelsthe SORA framework as a world simulator.

| 2018 data-sort-value="2024-01-03" | 2024 || [https://arxiv.org/abshtml/18032401.10122 World 04334v1 Large Language Modelsfor Robotics: Opportunities, Challenges, and Perspectives] || David Ha and Jürgen Schmidhuber J Wang, Z Wu, Y Li, H Jiang, P Shu, E Shi, H Hu et al. || agent builds a compact perspectives of using large language models in robotics, focusing on model of the world transparency, robustness, safety, and uses it to plan and dream, improving its performance in real environments. This aligns well with the interest in universal simulators-world applicability.

| 2020 data-sort-value="2024-01-04" | 2024 || [https://arxiv.org/abs/20032403.08934 NeRF09631 3D-VLA: Representing Scenes as Neural Radiance Fields for View SynthesisA 3D Vision-Language-Action Generative World Model] || Ben Mildenhall H Zhen, X Qiu, P Chen, J Yang, X Yan, Y Du et al. || highPresents 3D-fidelity views of complex 3D scenesVLA, a generative world model that combines vision, instrumental in creating synthetic data for roboticslanguage, and relevant for generating diverse visual environments for training robotsaction to guide robot control and achieve goal objectives.

| 2021 data-sort-value="2024-01-05" | 2024 || [https://arxiv.org/abs/21032402.11624 Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding02385 A Survey on Robotics with Foundation Models: Toward Embodied AI] || Krishna D. Kamath et al. Z Xu, K Wu, J Wen, J Li, N Liu, Z Che, J Tang || Focuses on predicting diverse future trajectoriesintegration of foundation models in robotics, crucial for creating realistic addressing safety and interpretation challenges in real-world scenarios , particularly in robotics simulationsdensely populated environments.

| data-sort-value="2024-01-06" | 2024 || [https://arxiv.org/abs/2402.05741 Real-world Robot Applications 06665 The Essential Role of Causality in Foundation World Models: A Reviewfor Embodied AI] || K KawaharazukaT Gupta, T Matsushima W Gong, C Ma, N Pawlowski, A Hilmkil et al. || overview of the practical application importance of causality in foundation world models in real-world roboticsfor embodied AI, including predicting that these models will simplify the integration introduction of specific components within existing robot systemsnew robots into everyday life.

| data-sort-value="2024-01-07" | 2024 || [https://arxiv.org/abs/24052306.03520 Is SORA a World Simulator? A Comprehensive Survey on General 06561 Learning World Models and Beyondwith Identifiable Factorization] || Y Liu, B Huang, Z Zhu, X Wang, W Zhao, C Min, N Deng, M Dou H Tian et al. || surveys the applications of a world models in various fields, including roboticsmodel with identifiable blocks, and discusses ensuring the potential removal of the SORA framework as a world simulatorredundancies.

| data-sort-value="2024-01-08" | 2024 || [https://arxiv.org/abs/24032311.09631 Large Language 09064 Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models for Robotics: Opportunities, Challenges, and Perspectives] || J Wang, Z Wu, Y LiKim, H JiangG Singh, P Shu, E Shi, H Hu J Park et al. || perspectives of using large language systematic generalization in vision models in robotics, focusing on model transparency, robustness, safety, and real-world applicabilitymodels.

| 2024 data-sort-value="2020-01-01" | 2020 || [https://arxiv.org/abs/24032003.09631 3D-VLA08934 NeRF: A 3D Vision-Language-Action Generative World ModelRepresenting Scenes as Neural Radiance Fields for View Synthesis] || H Zhen, X Qiu, P Chen, J Yang, X Yan, Y Du Ben Mildenhall et al. || Presents high-fidelity views of complex 3D-VLA, a generative world model that combines visionscenes, languageinstrumental in creating synthetic data for robotics, and action to guide robot control and achieve goal objectivesrelevant for generating diverse visual environments for training robots.

| 2024 data-sort-value="2018-01-01" | 2018 || [https://arxiv.org/abs/24021803.02385 A Survey on Robotics with Foundation 10122 World Models: Toward Embodied AI] || Z Xu, K Wu, J Wen, J Li, N Liu, Z Che, J Tang David Ha and Jürgen Schmidhuber || integration agent builds a compact model of foundation models in roboticsthe world and uses it to plan and dream, addressing safety and interpretation challenges improving its performance in real-world scenarios, particularly environments. This aligns well with the interest in densely populated environmentsuniversal simulators.

| 2024 || [https://arxiv.org/abs/2402.06665 The Essential Role of Causality in Foundation World Models for Embodied AI] || T Gupta, W Gong, C Ma, N Pawlowski, A Hilmkil et al. || importance of causality in foundation world models for embodied AI, predicting that these models will simplify the introduction of new robots into everyday life.|data-sort-value="2017-01-01" | 2024 2017 || [https://arxiv.org/abs/23061612.06561 07828 Learning World Models with Identifiable Factorizationfrom Simulated and Unsupervised Images through Adversarial Training] || Y Liu, B Huang, Z Zhu, H Tian Ashish Shrivastava et al. || a world model with identifiable blockstechnique that refines simulated images to make them more realistic using adversarial training, ensuring enhancing the removal quality of redundancies .|-| 2024 || [https://arxiv.org/abs/2311.09064 Imagine the Unseen World: A Benchmark synthetic data for Systematic Generalization in Visual World Models] || Y Kim, G Singh, J Park et al. || systematic generalization in vision models and world training robotics models.