# AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility

arXiv: https://arxiv.org/abs/2409.15783 · HTML: https://mlsanity.com/paper/2409.15783/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2024.

## Built on by (most cited first, 13 of 13)

- [ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills](https://mlsanity.com/paper/2502.01143/) (2025)
- [The One RING: a Robotic Indoor Navigation Generalist](https://mlsanity.com/paper/2412.14401/) (2024)
- [Ego-Vision World Model for Humanoid Contact Planning](https://mlsanity.com/paper/2510.11682/) (2025)
- [Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation](https://mlsanity.com/paper/2603.15759/) (2026)
- [A Champion-level Vision-based Reinforcement Learning Agent for Competitive Racing in Gran Turismo 7](https://mlsanity.com/paper/2504.09021/) (2025)
- [Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics](https://mlsanity.com/paper/2502.07380/) (2025)
- [Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing](https://mlsanity.com/paper/2505.07321/) (2025)
- [MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning](https://mlsanity.com/paper/2510.03142/) (2025)
- [MVAdapt: Zero-Shot Multi-Vehicle Adaptation for End-to-End Autonomous Driving](https://mlsanity.com/paper/2604.11854/) (2026)
- [KING: Embodiment-Aware Kinematic Graph Neural Network for Unified Motion Representation of Legged and Wheeled Robots](https://mlsanity.com/paper/2608.01015/) (2026)
- [Dynamics Distillation for Efficient and Transferable Control Learning](https://mlsanity.com/paper/2605.01516/) (2026)
- [Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains](https://mlsanity.com/paper/2607.13319/) (2026)
- [SRL-MPC: Shape-Aware Reinforcement Learned Model Predictive Control](https://mlsanity.com/paper/2608.21175/) (2026)

## Abstract (arXiv)

Recent works in the robot learning community have successfully introduced generalist models capable of controlling various robot embodiments across a wide range of tasks, such as navigation and locomotion. However, achieving agile control, which pushes the limits of robotic performance, still relies on specialist models that require extensive parameter tuning. To leverage generalist-model adaptability and flexibility while achieving specialist-level agility, we propose AnyCar, a transformer-based generalist dynamics model designed for agile control of various wheeled robots. To collect training data, we unify multiple simulators and leverage different physics backends to simulate vehicles with diverse sizes, scales, and physical properties across various terrains. With robust training and real-world fine-tuning, our model enables precise adaptation to different vehicles, even in the wild and under large state estimation errors. In real-world experiments, AnyCar shows both few-shot and zero-shot generalization across a wide range of vehicles and environments, where our model, combined with a sampling-based MPC, outperforms specialist models by up to 54%. These results represent a key step toward building a foundation model for agile wheeled robot control. We will also open-source our framework to support further research.