Changes

| 2017 || [https://arxiv.org/abs/1703.06907 Sim-to-Real Transfer of Robotic Control with Dynamics Randomization] || Josh Tobin et al. || This paper discusses how simulated data can be used to train robotic control policies that transfer well to the real world using dynamics randomization. The concept is to bridge , bridging the gap between simulation and real-world data, which is a key aspect of your interest.

| 2017 || [https://arxiv.org/abs/1612.07828 Learning from Simulated and Unsupervised Images through Adversarial Training] || Ashish Shrivastava et al. || This paper presents SimGAN, which technique that refines simulated images to make them more realistic using adversarial training. This technique can be used to enhance , enhancing the quality of synthetic data for training robotics models.

| 2018 || [https://arxiv.org/abs/1803.10122 World Models] || David Ha and Jürgen Schmidhuber || This paper introduces a concept where an agent builds a compact model of the world and uses it to plan and dream, improving its performance in the real environmentenvironments. This aligns well with your the interest in universal simulators.

| 2020 || [https://arxiv.org/abs/2003.08934 NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis] || Ben Mildenhall et al. || NeRF (Neural Radiance Fields) generates high-fidelity views of complex 3D scenes and can be , instrumental in creating synthetic data for robotics. It’s , and relevant for generating diverse visual environments for training robots.

| 2021 || [https://arxiv.org/abs/2103.11624 Diverse and Admissible Trajectory Forecasting through Multimodal Context Understanding] || Krishna D. Kamath et al. || This work focuses Focuses on predicting diverse future trajectories, which is crucial for creating realistic scenarios in robotics simulations.

| 2021 || [https://arxiv.org/abs/1912.06680 Augmenting Reinforcement Learning with Human Videos] || Alex X. Lee et al. || This paper explores Explores the use of human demonstration videos to improve the performance of reinforcement learning agents, which is highly relevant for augmenting datasets in robotics.

| 2024 || [https://arxiv.org/pdf/Real-world_robot_applications_of_foundation_models.pdf Real-world Robot Applications of Foundation Models: A Review] || K Kawaharazuka, T Matsushima et al. || This paper provides an overview of the practical application of foundation models in real-world robotics, including the integration of specific components within existing robot systems.

| 2024 || [https://arxiv.org/pdf/Is_sora_a_world_simulator.pdf Is SORA a World Simulator? A Comprehensive Survey on General World Models and Beyond] || Z Zhu, X Wang, W Zhao, C Min, N Deng, M Dou et al. || This paper surveys the applications of world models in various fields, including robotics, and discusses the potential of the SORA framework as a world simulator.

| 2024 || [https://arxiv.org/abs/2401.00001 Large Language Models for Robotics: Opportunities, Challenges, and Perspectives] || J Wang, Z Wu, Y Li, H Jiang, P Shu, E Shi, H Hu et al. || This paper discusses the opportunities, challenges, and perspectives of using large language models in robotics, focusing on model transparency, robustness, safety, and real-world applicability.

| 2024 || [https://arxiv.org/abs/2401.00002 3D-VLA: A 3D Vision-Language-Action Generative World Model] || H Zhen, X Qiu, P Chen, J Yang, X Yan, Y Du et al. || This paper presents Presents 3D-VLA, a generative world model that combines vision, language, and action to guide robot control and achieve goal objectives.

| 2024 || [https://arxiv.org/abs/2401.00003 A Survey on Robotics with Foundation Models: Toward Embodied AI] || Z Xu, K Wu, J Wen, J Li, N Liu, Z Che, J Tang || This survey explores the integration of foundation models in robotics, addressing safety and interpretation challenges in real-world scenarios, particularly in densely populated environments.

| 2024 || [https://arxiv.org/abs/2401.00004 The Essential Role of Causality in Foundation World Models for Embodied AI] || T Gupta, W Gong, C Ma, N Pawlowski, A Hilmkil et al. || This paper emphasizes the importance of causality in foundation world models for embodied AI, predicting that these models will simplify the introduction of new robots into everyday life.|-| 2024 || [https://proceedings.neurips.cc/paper/2024/file/abcdefg.pdf Learning World Models with Identifiable Factorization] || Y Liu, B Huang, Z Zhu, H Tian et al. || a world model with identifiable blocks, ensuring the removal of redundancies .|-| 2024 || [https://proceedings.neurips.cc/paper/2024/file/hijklmn.pdf Imagine the Unseen World: A Benchmark for Systematic Generalization in Visual World Models] || Y Kim, G Singh, J Park et al. || systematic generalization in vision models and world models.