# Learning Terrain-Aware Kinodynamic Model for Autonomous Off-Road Rally Driving With Model Predictive Path Integral Control

arXiv: https://arxiv.org/abs/2305.00676 · HTML: https://mlsanity.com/paper/2305.00676/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning](https://mlsanity.com/paper/1708.02596/) (2017)
- [BADGR: An Autonomous Self-Supervised Learning-Based Navigation System](https://mlsanity.com/paper/2002.05700/) (2020)
- [Information Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving](https://mlsanity.com/paper/1707.02342/) (2017)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [TartanDrive: A Large-Scale Dataset for Learning Off-Road Dynamics Models](https://mlsanity.com/paper/2205.01791/) (2022)
- [AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification](https://mlsanity.com/paper/2110.13511/) (2021)
- [Learning Inverse Kinodynamics for Accurate High-Speed Off-Road Navigation on Unstructured Terrain](https://mlsanity.com/paper/2102.12667/) (2021)
- [GrASPE: Graph based Multimodal Fusion for Robot Navigation in Unstructured Outdoor Environments](https://mlsanity.com/paper/2209.05722/) (2022)
- [VI-IKD: High-Speed Accurate Off-Road Navigation using Learned Visual-Inertial Inverse Kinodynamics](https://mlsanity.com/paper/2203.15983/) (2022)
- [ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environments](https://mlsanity.com/paper/2209.06522/) (2022)
- [Learning Ground Traversability from Simulations](https://mlsanity.com/paper/1709.05368/) (2017)
- [Smooth Model Predictive Path Integral Control without Smoothing](https://mlsanity.com/paper/2112.09988/) (2021)
- [Multimodal dynamics modeling for off-road autonomous vehicles](https://mlsanity.com/paper/2011.11751/) (2020)

## Built on by (most cited first, 8 of 8)

- [EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy](https://mlsanity.com/paper/2311.06234/) (2023)
- [Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving](https://mlsanity.com/paper/2504.16923/) (2025)
- [TADPO: Reinforcement Learning Goes Off-road](https://mlsanity.com/paper/2603.05995/) (2026)
- [T-CBF: Traversability-based Control Barrier Function to Navigate Vertically Challenging Terrain](https://mlsanity.com/paper/2503.06083/) (2025)
- [Learning the Right Abstraction: Neural Reduced Dynamics for Complex Robot Control](https://mlsanity.com/paper/2608.19375/) (2026)
- [Data-Driven Modeling and Correction of Vehicle Dynamics](https://mlsanity.com/paper/2512.00289/) (2025)
- [Adapting Generalist Vehicle Models for High-Speed MPC Across Terrains](https://mlsanity.com/paper/2607.13319/) (2026)
- [Continual Learning for Traversability Prediction with Uncertainty-Aware Adaptation](https://mlsanity.com/paper/2609.17141/) (2026)

## Abstract (arXiv)

High-speed autonomous driving in off-road environments has immense potential for various applications, but it also presents challenges due to the complexity of vehicle-terrain interactions. In such environments, it is crucial for the vehicle to predict its motion and adjust its controls proactively in response to environmental changes, such as variations in terrain elevation. To this end, we propose a method for learning terrain-aware kinodynamic model which is conditioned on both proprioceptive and exteroceptive information. The proposed model generates reliable predictions of 6-degree-of-freedom motion and can even estimate contact interactions without requiring ground truth force data during training. This enables the design of a safe and robust model predictive controller through appropriate cost function design which penalizes sampled trajectories with unstable motion, unsafe interactions, and high levels of uncertainty derived from the model. We demonstrate the effectiveness of our approach through experiments on a simulated off-road track, showing that our proposed model-controller pair outperforms the baseline and ensures robust high-speed driving performance without control failure.