Collect some World Models for Autonomous Driving (and Robotic) papers.
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Updated
Apr 11, 2025
Collect some World Models for Autonomous Driving (and Robotic) papers.
[NeurIPS 2024] A Generalizable World Model for Autonomous Driving
Code for "TD-MPC2: Scalable, Robust World Models for Continuous Control"
[CVPR 2024 Highlight] Visual Point Cloud Forecasting
A skill-based platform for ROS v.2 with knowledge representating, planning and reasoning
Code for "Hierarchical World Models as Visual Whole-Body Humanoid Controllers"
Build, evaluate and run General Multi-Agent Assistance with ease
Official repository for "iVideoGPT: Interactive VideoGPTs are Scalable World Models" (NeurIPS 2024), https://arxiv.org/abs/2405.15223
[NeurIPS 2024] Agent Planning with World Knowledge Model
A general AI agent framework that can be adapted to various tasks and environments.
A comprehensive list of papers for the definition of World Models and using World Models for General Video Generation, Embodied AI, and Autonomous Driving, including papers, codes, and related websites.
[ICLR 2025 Oral] PyTorch code for the paper "Open-World Reinforcement Learning over Long Short-Term Imagination"
Official Implementation for the paper "SR-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models"
Official Implementation of DINO-Foresight: Looking into the Future with DINO
The PyTorch implementation of paper: "AdaWorld: Learning Adaptable World Models with Latent Actions".
Official implementation of Neural Lithography (SIGGRAPH Asia 2023)
A framework for training world models with virtual environments, complete with annotated environment dataset (RetroAct), exploration agent (AutoExplore Agent), and GenieRedux-G - an implementation of Genie with enhancements
Official repo of the ICLR 2025 paper "MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos"
Official code for the ICLR 2025 paper, "Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining"
ACT-Bench – We Evaluate Action-Fidelity of World Models for Autonomous Driving
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