#Simulation

Discover a comprehensive platform with over 200 environments and more than 2600 realistic objects tailored for embodied AI research. This framework supports multiple agents and a variety of AI tasks across diverse image settings, bridging simulation and real-world scenarios in a neutral and effective manner.

Explore a state-of-the-art simulation-rendering framework that effectively utilizes video diffusion models for decoding the dynamics of 3D Gaussian physics. The project includes available training code, synthetic datasets, and forthcoming additions such as tutorials and demonstrations. Benefit from pre-trained 3D Gaussian model integration and text-to-video diffusion model optimization for enhanced physical parameter tuning. This tool, underpinned by valuable open-source methodologies and datasets, serves as a robust platform for advancing physics simulation research.

Delve into the world of AI-driven games by the EmbraceAGI community, offering a range of gameplay from life simulations powered by GPT-4 to time-travel adventures in World End Simulator. Unravel mysteries with Rule Ghost Talk, rewrite history with Civilization, or engage in elemental combat in the Five Elements duel. Experience varied gaming narratives and community-driven exploration.

VirtualHome offers a Python API-driven platform to simulate household activities, focusing on complex agent interactions in dynamic, procedurally-generated environments. With features like realistic physics and multi-agent support, and updates enhancing graphics and time systems, it's ideal for embodied AI research. Explore detailed documentation to leverage its rich simulation functions.

This framework enables research into learning from human feedback using methods like RLHF, supporting feedback simulation and automated evaluations. It offers reference implementations for developers and researchers, facilitating research into instruction-aligned models. The framework is compatible with multiple language models, including GPT-4, and focuses on simulation accuracy for improved model evaluation and development.

Vista presents a generalizable model for simulating various driving scenarios with high accuracy. It predicts over long durations, performs complex actions like steering and speed decisions, and offers feedback without ground truth data. Recent updates have improved user accessibility through new model weights, installation guides, and documentation. The online demo showcases its capabilities in simulated environments, advancing autonomous driving technology.