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Audio Overview (generated using NotebookLM)
Our Approach
AnyCar System Pipeline. (1) Data Collection: We collect data in 4 different simulations to pre-train the AnyCar model. We separately also collect real-world data for fine-tuning of the model. (2) Model Training: We pre-train the model with the simulation dataset and enhance prediction robustness through masking, adding noise, and attacking the inputs. (3) Deployment: We fine-tune the model with few-shot real-world data to achieve zero-shot transfer across different vehicles, environmental conditions, and state estimation methods.
Deployment In the Wild
Deployment In Uneven Terrain
Large-Scale Pre-training in Diverse Simulation
Few-shot Real-world Fine-tuning (Teleoperation)
Out-of-Domain Adaptation
One More Thing ...
We Open-Source Our 1/16 Car Design for Wheeled Agility Research
BibTeX
@misc{xiao2024anycaranywherelearninguniversal,
title={AnyCar to Anywhere: Learning Universal Dynamics Model for Agile and Adaptive Mobility},
author={Wenli Xiao and Haoru Xue and Tony Tao and Dvij Kalaria and John M. Dolan and Guanya Shi},
year={2024},
eprint={2409.15783},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2409.15783},
}