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VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain

Tong Xu, Chenhui Pan, Aniket Datar, Xuesu Xiao

TL;DR

VertiAdaptor is proposed, a novel online adaptation framework that efficiently integrates elevation with semantic embeddings to enable terrain-aware kinodynamic modeling and planning via function encoders and improves prediction accuracy by up to 23.9% and achieves a 5X faster adaptation time, advancing the robustness and reliability of autonomous robots in complex and evolving off-road environments.

Abstract

Autonomous driving in off-road environments presents significant challenges due to the dynamic and unpredictable nature of unstructured terrain. Traditional kinodynamic models often struggle to generalize across diverse geometric and semantic terrain types, underscoring the need for real-time adaptation to ensure safe and reliable navigation. We propose VertiAdaptor (VA), a novel online adaptation framework that efficiently integrates elevation with semantic embeddings to enable terrain-aware kinodynamic modeling and planning via function encoders. VA learns a kinodynamic space spanned by a set of neural ordinary differential equation basis functions, capturing complex vehicle-terrain interactions across varied environments. After offline training, the proposed approach can rapidly adapt to new, unseen environments by identifying kinodynamics in the learned space through a computationally efficient least-squares calculation. We evaluate VA within the Verti-Bench simulator, built on the Chrono multi-physics engine, and validate its performance both in simulation and on a physical Verti-4-Wheeler platform. Our results demonstrate that VA improves prediction accuracy by up to 23.9% and achieves a 5X faster adaptation time, advancing the robustness and reliability of autonomous robots in complex and evolving off-road environments.

VertiAdaptor: Online Kinodynamics Adaptation for Vertically Challenging Terrain

TL;DR

VertiAdaptor is proposed, a novel online adaptation framework that efficiently integrates elevation with semantic embeddings to enable terrain-aware kinodynamic modeling and planning via function encoders and improves prediction accuracy by up to 23.9% and achieves a 5X faster adaptation time, advancing the robustness and reliability of autonomous robots in complex and evolving off-road environments.

Abstract

Autonomous driving in off-road environments presents significant challenges due to the dynamic and unpredictable nature of unstructured terrain. Traditional kinodynamic models often struggle to generalize across diverse geometric and semantic terrain types, underscoring the need for real-time adaptation to ensure safe and reliable navigation. We propose VertiAdaptor (VA), a novel online adaptation framework that efficiently integrates elevation with semantic embeddings to enable terrain-aware kinodynamic modeling and planning via function encoders. VA learns a kinodynamic space spanned by a set of neural ordinary differential equation basis functions, capturing complex vehicle-terrain interactions across varied environments. After offline training, the proposed approach can rapidly adapt to new, unseen environments by identifying kinodynamics in the learned space through a computationally efficient least-squares calculation. We evaluate VA within the Verti-Bench simulator, built on the Chrono multi-physics engine, and validate its performance both in simulation and on a physical Verti-4-Wheeler platform. Our results demonstrate that VA improves prediction accuracy by up to 23.9% and achieves a 5X faster adaptation time, advancing the robustness and reliability of autonomous robots in complex and evolving off-road environments.
Paper Structure (21 sections, 8 equations, 4 figures, 5 tables, 1 algorithm)

This paper contains 21 sections, 8 equations, 4 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Off-road Mobility
  4. Meta Learning in Robotics
  5. Method
  6. Problem Formulation
  7. Elevation & Semantic Embeddings
  8. Function Encoder for Neural ODEs
  9. VertiAdaptor (VA)
  10. Offline Training
  11. Online Adaptation
  12. Implementations
  13. Datasets and Preprocessing
  14. Model Architecture
  15. Baselines
  16. ...and 6 more sections

Figures (4)

  • Figure 1: Facing a variety of constantly changing elevation and semantics on vertically challenging, off-road terrain, autonomous mobile robots need to quickly adapt their terrain understanding through online kinodynamics adaptation to achieve safe and efficient navigation.
  • Figure 2: VertiAdaptor Overview: The offline training phase combines elevation and semantic embeddings to train neural ODE basis functions, i.e., state change rates $\{g_1, g_2, \ldots, g_k\}$ integrated into state changes $\{G_1, G_2, \ldots, G_k\}$. The online adaptation phase uses a small set of new data to identify the coefficients, enabling kinodynamics to be represented as a linear combination of basis functions.
  • Figure 3: Model Prediction Error of VA and three Baselines: Average One-Step 6-DoF Positional and Angular Error (Left); Prediction Error vs. Prediction Step (Middle and Right).
  • Figure 4: 6-DoF Vehicle Trajectories of VA, MAML, and Ground Truth with Increasing Horizon: VA matches Ground Truth even with a long horizon, while MAML diverges more.