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AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control

arXiv 2510.05443 ↗First posted 6 Oct 2025cs.RO cs.LG cs.SYcited by 0

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Abstract (arXiv)

Mobile robots, such as ground vehicles and quadrotors, are becoming increasingly important in various fields, from logistics to agriculture, where they automate processes in environments that are difficult to access for humans. However, to perform effectively in uncertain environments using model-based controllers, these systems require dynamics models capable of responding to environmental variations, especially when direct access to environmental information is limited. To enable such adaptivity and facilitate integration with model predictive control, we propose an adaptive dynamics model which bypasses the need for direct environmental knowledge by inferring operational environments from state-action history. The dynamics model is based on neural ordinary equations, and a two-phase training procedure is used to learn latent environment representations. We demonstrate the effectiveness of our approach through goal-reaching and path-tracking tasks on three robotic platforms of increasing complexity: a 2D differential wheeled robot with changing wheel contact conditions, a 3D quadrotor in variational wind fields, and the Sphero BOLT robot under two contact conditions for real-world deployment. Empirical results corroborate that our method can handle temporally and spatially varying environmental changes in both simulation and real-world systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 35 references that are in the map. First posted on arXiv 6 Oct 2025.

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28 of 35 references.

References · 28

  1. 2022Flow Matching for Generative Modelingcited by 5,426
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 748
  4. 2019Hamiltonian Neural Networkscited by 702
  5. 2021RMA: Rapid Motor Adaptation for Legged Robotscited by 430
  6. 2020Lagrangian Neural Networkscited by 388
  7. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 270
  8. 2018Learning to Walk via Deep Reinforcement Learningcited by 153
  9. 2022Legged Locomotion in Challenging Terrains using Egocentric Visioncited by 131
  10. 2022In-Hand Object Rotation via Rapid Motor Adaptationcited by 102
  11. 2020Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learningcited by 95
  12. 2019Learning Compositional Koopman Operators for Model-Based Controlcited by 67
16 more
  1. 2018Propagation Networks for Model-Based Control Under Partial Observationcited by 64
  2. 2019Data Efficient Reinforcement Learning for Legged Robotscited by 60
  3. 2021Continuous-Time Model-Based Reinforcement Learningcited by 39
  4. 2020Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learningcited by 34
  5. 2020Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloadscited by 27
  6. 2022Context is Everything: Implicit Identification for Dynamics Adaptationcited by 26
  7. 2024Trajectory Flow Matching with Applications to Clinical Time Series Modelingcited by 21
  8. 2021Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotorscited by 18
  9. 2022Online Dynamics Learning for Predictive Control with an Application to Aerial Robotscited by 17
  10. 2022Learning a Single Near-hover Position Controller for Vastly Different Quadcopterscited by 16
  11. 2024Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layoutcited by 14
  12. 2024A Learning-based Quadcopter Controller with Extreme Adaptationcited by 12
  13. 2023DATT: Deep Adaptive Trajectory Tracking for Quadrotor Controlcited by 11
  14. 2024Dynamics as Prompts: In-Context Learning for Sim-to-Real System Identificationscited by 10
  15. 2025Robust Simulation-Based Inference under Missing Data via Neural Processescited by 5
  16. 2024Environmentally Adaptive Control Including Variance Minimization Using Stochastic Predictive Network with Parametric Bias: Application to Mobile Robotscited by 1

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