| 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.

| data-sort-value="2024-01-05" | 2024 || [https://arxiv.org/abs/2402.05741 Real-world Robot Applications of 02385 A Survey on Robotics with Foundation Models: A ReviewToward Embodied AI] || Z Xu, K KawaharazukaWu, J Wen, J Li, N Liu, Z Che, T Matsushima et al. J Tang || overview of the practical application integration of foundation models in robotics, addressing safety and interpretation challenges in real-world roboticsscenarios, including the integration of specific components within existing robot systemsparticularly in densely populated environments.

| data-sort-value="2024-01-06" | 2024 || [https://arxiv.org/abs/24052402.03520 Is SORA a World Simulator? A Comprehensive Survey on General 06665 The Essential Role of Causality in Foundation World Models and Beyondfor Embodied AI] || Z Zhu, X WangT Gupta, W ZhaoGong, C MinMa, N DengPawlowski, M Dou A Hilmkil et al. || surveys the applications importance of causality in foundation world models in various fieldsfor embodied AI, including robotics, and discusses predicting that these models will simplify the potential introduction of the SORA framework as a world simulatornew robots into everyday life.

| data-sort-value="2024-01-07" | 2024 || [https://arxiv.org/abs/24032306.09631 Large Language 06561 Learning World Models for Robotics: Opportunities, Challenges, and Perspectiveswith Identifiable Factorization] || J Wang, Z Wu, Y LiLiu, H JiangB Huang, P Shu, E ShiZ Zhu, H Hu Tian et al. || perspectives of using large language models in robotics, focusing on a world model transparencywith identifiable blocks, robustness, safety, and real-world applicabilityensuring the removal of redundancies.

| data-sort-value="2024-01-08" | 2024 || [https://arxiv.org/abs/24032311.09631 3D-VLA09064 Imagine the Unseen World: A 3D Vision-Language-Action Generative Benchmark for Systematic Generalization in Visual World ModelModels] || H Zhen, X QiuY Kim, P ChenG Singh, J Yang, X Yan, Y Du Park et al. || Presents 3D-VLA, a generative world model that combines systematic generalization in vision, language, models and action to guide robot control and achieve goal objectivesworld models.

| 2024 data-sort-value="2020-01-01" | 2020 || [https://arxiv.org/abs/24022003.02385 A Survey on Robotics with Foundation Models08934 NeRF: Toward Embodied AIRepresenting Scenes as Neural Radiance Fields for View Synthesis] || Z Xu, K Wu, J Wen, J Li, N Liu, Z Che, J Tang Ben Mildenhall et al. || integration high-fidelity views of foundation models complex 3D scenes, instrumental in creating synthetic data for robotics, addressing safety and interpretation challenges in real-world scenarios, particularly in densely populated relevant for generating diverse visual environmentsfor training robots.

| 2024 data-sort-value="2018-01-01" | 2018 || [https://arxiv.org/abs/24021803.06665 The Essential Role of Causality in Foundation 10122 World Models for Embodied AI] || T Gupta, W Gong, C Ma, N Pawlowski, A Hilmkil et al. David Ha and Jürgen Schmidhuber || importance agent builds a compact model of causality in foundation the world models for embodied AIand uses it to plan and dream, predicting that these models will simplify improving its performance in real environments. This aligns well with the introduction of new robots into everyday lifeinterest in universal simulators.

| 2024 data-sort-value="2017-01-01" | 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.