Robot Dynamics Learning

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Overview

Robot dynamics learning studies how to predict a robot’s motion and its interactions with objects and environments from states, actions, and sensor data. Methods range from physics-informed models such as Lagrangian and Koopman networks to recurrent models, graph neural networks, Gaussian processes, and learned world models; many combine physical structure with neural residuals or uncertainty estimates. These models support model-predictive control, reinforcement learning, manipulation, and sim-to-real transfer, with evaluation spanning MuJoCo and manipulation benchmarks as well as real arms, legged robots, and soft robots. A central challenge is maintaining accurate, stable long-horizon predictions through contact and changing conditions while keeping models efficient and transferable across tasks and robot morphologies.

Latest papers

CardsList

90 papers26 papers in the last four weeks, up 86% on the four weeks before. 0.2% of all new papers.

  1. Adapting Neural Robot Dynamics on the Fly for Predictive Control

    Apr 5, 2026Abdullah Altawaitan, Nikolay AtanasovRobot Dynamics LearningRobot Dynamics Identification

    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA · Kuwait University

  2. A Flow Matching Framework for Soft-Robot Inverse Dynamics

    Apr 3, 2026Hang Yang, Fangju Yang, Yangming Zhang +5Robot Dynamics LearningInverse Dynamics

  3. CLaD: Planning with Grounded Foresight via Cross-Modal Latent Dynamics

    Mar 31, 2026Andrew Jeong, Jaemin Kim, Sebin Lee +1Robot Motion PlanningCross-Modal Representation Learning

    KAIST

  4. Learning Actuator-Aware Spectral Submanifolds for Precise Control of Continuum Robots

    Mar 24, 2026Paul Leonard Wolff, Hugo Buurmeijer, Luis Pabon +6Continuum Robot ControlRobot Dynamics Learning

    Autonomous Systems Lab, Stanford University, USA. · Soft Robotics Lab, ETH Zurich, Switzerland. · Institute of Mechanical Systems, ETH Zurich, Switzerland. +1

  5. Wake Up to the Past: Using Memory to Model Fluid Wake Effects on Robots

    Mar 23, 2026Luca Vendruscolo, Eduardo Sebastián, Amanda Prorok +1Multi-Robot SystemsRobot Dynamics Learning

    Department of Computer Science and Technology, University of Cambridge, UK.

  6. RAFL: Generalizable Sim-to-Real of Soft Robots with Residual Acceleration Field Learning

    Mar 23, 2026Dong Heon Cho, Boyuan ChenSynthetic-to-Real Domain AdaptationResidual Dynamics Learning

    Department of Computer Science, Duke University, 308 Research Dr., Durham, 27705, NC, USA · Department of Mechanical Engineering and Material Science, 121 Hudson Hall, Durham, 27708, NC, USA · Department of Electrical and Computer Engineering, 100 Science Dr., Durham, 27708, NC, USA

  7. Accurate Open-Loop Control of a Soft Continuum Robot Using Visually Learned Latent Dynamics

    Mar 20, 2026Henrik Krauss, Johann Licher, Naoya Takeishi +2Robotic ControlContinuum Robot Control

    Department of Advanced Interdisciplinary Studies, The University of Tokyo, Tokyo, 153-8904, Japan. · Institute of Assembly Technology and Robotics, Leibniz University Hannover, 30823 Germany. · Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, 153-8904, Japan.

  8. Rapid Adaptation of Particle Dynamics for Generalized Deformable Object Mobile Manipulation

    Mar 18, 2026Bohan Wu, Roberto Martín-Martín, Li Fei-FeiDeformable Object ManipulationGeneralization in Robotic Manipulation

    the University of Texas at Austin, USA

  9. Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation

    Mar 18, 2026Lars Bartels, Amon Lahr, Andrea Carron +1Model Predictive ControlGaussian Processes

    Institute for Dynamic Systems and Control, ETH Zürich, CH-8092 Zürich, Switzerland.

  10. Zero-Shot Generalization from Motion Demonstrations to New Tasks

    Mar 16, 2026Kilian Freitag, Alvin Combrink, Nadia FigueroaRobot Policy GeneralizationRobot Skill Learning

    Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden · GRASP Lab, University of Pennsylvania, PA 19104, USA

  11. Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation

    Mar 16, 2026Amos Alwala, Yuchen Hu, Gabriel da Silva Lima +1Robotic ControlRobot Trajectory Tracking

    With the Smart Systems, Department of Mechanical and Materials Engineering, Faculty of Technology, University of Turku, 20520 Turku, Finland.

  12. WestWorld: A Knowledge-Encoded Scalable Trajectory World Model for Diverse Robotic Systems

    Mar 15, 2026Yuchen Wang, Jiangtao Kong, Sizhe Wei +6Robot Motion PredictionWorld Models for Robotics

    William & Mary · Georgia Institute of Technology · Carnegie Mellon University +2

  13. Data-Driven Physics Embedded Dynamics with Predictive Control and Reinforcement Learning for Quadrupeds

    Mar 15, 2026Prakrut Kotecha, Aditya Shirwatkar, Shishir KolathayaLagrangian Neural NetworksRL for Legged Locomotion

    the Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science, Bengaluru. · the Robert Bosch Center for Cyber-Physical Systems and the Department of Computer Science & Automation, Indian Institute of Science, Bengaluru.

  14. MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuvers

    Mar 11, 2026Jin Zhou, Dongcheng Cao, Xian Wang +1Robot Policy AdaptationQuadrotor Control

    Authors are with the College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

  15. Emerging Extrinsic Dexterity in Cluttered Scenes via Dynamics-aware Policy Learning

    Mar 10, 2026Yixin Zheng, Jiangran Lyu, Yifan Zhang +8Non-Prehensile Robotic ManipulationRobotic Manipulation

    Institute of Automation, Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · Galbot +2

  16. STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching

    Mar 9, 2026Prakrut Kotecha, Ganga Nair B, Shishir KolathayaLagrangian Neural NetworksRobot Dynamics Learning

    the Robert Bosch Center for Cyber-Physical Systems, Indian Institute of Science, Bengaluru · the Robert Bosch Center for Cyber-Physical Systems and the Department of Computer Science & Automation, Indian Institute of Science, Bengaluru

  17. Dual-Horizon Hybrid Internal Model for Low-Gravity Quadrupedal Jumping with Hardware-in-the-Loop Validation

    Mar 9, 2026Haozhe Xu, Yifei Zhao, Wenhao Feng +6Robot LocomotionQuadruped Locomotion

  18. Few-Shot Neural Differentiable Simulator: Real-to-Sim Rigid-Contact Modeling

    Mar 6, 2026Zhenhao Huang, Siyuan Luo, Bingyang Zhou +3Differentiable PhysicsRobot Dynamics Learning

    National University of Singapore, Singapore · Prana Lab, San Francisco, USA

  19. Data-Driven Control of a Magnetically Actuated Fish-Like Robot

    Mar 5, 2026Akiyuki Koyama, Hiroaki KawashimaRobotic ControlUnderwater Robotics

    Graduate School of Information Science, University of Hyogo, Japan

  20. Tendon Force Modeling for Sim2Real Transfer of Reinforcement Learning Policies for Tendon-Driven Robots

    Mar 4, 2026Valentin Yuryev, Josie HughesSim-to-Real TransferTendon-Driven Actuation

    These authors are with with CREATE Lab, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland.

  21. Whole-Body Safe Control of Robotic Systems with Koopman Neural Dynamics

    Mar 4, 2026Sebin Jung, Abulikemu Abuduweili, Jiaxing Li +1Robotic ControlRobot Safety

    Robotics Institute, Carnegie Mellon University, Pittsburgh, PA.

  22. Shape-Interpretable Visual Self-Modeling Enables Geometry-Aware Continuum Robot Control

    Mar 2, 2026Peng Yu, Xin Wang, Ning TanContinuum Robot ControlVision-Based Robot Control

    School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.

  23. Physics-Embedded Neural ODEs for Learning Antagonistic Pneumatic Artificial Muscle Dynamics

    Feb 27, 2026Xinyao Wang, Jonathan RealmutoPneumatic ActuationSoft Pneumatic Actuators

    Dept. of Mechanical Engineering, University of California, Riverside, CA, USA

  24. CableRobotGraphSim: A Graph Neural Network for Modeling Partially Observable Cable-Driven Robot Dynamics

    Feb 24, 2026Nelson Chen, William R. Johnson, Rebecca Kramer-Bottiglio +2Robotics SimulationRobot Dynamics Learning

    1Rutgers University · 2Yale University

  25. Scaling Law of Neural Koopman Operators

    Feb 23, 2026Abulikemu Abuduweili, Yuyang Pang, Feihan Li +1Koopman Operator LearningRobot Dynamics Learning

    Authors are with the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

  26. CLASH: Collision Learning via Augmented Sim-to-real Hybridization to Bridge the Reality Gap

    Feb 21, 2026Haotian He, Ning Guo, Siqi Shi +2Sim-to-Real TransferRobotics Simulation

    School of Mathematical Sciences, Peking University. · School of Artificial Intelligence, Shanghai Jiao Tong University. · School of Mathematical Sciences, Beijing Normal University.

  27. Learning Robust Visuomotor Policies via Dynamics Alignment

    Oct 31, 2025Dohyeok Lee, Jung Min Lee, Munkyung Kim +7Visuomotor Policy LearningFlow-Based Policy Learning

    Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea · Department of Statistics, Korea University, Seoul 02841, Korea

  28. Model-Based Adaptive Precision Control for Tabletop Planar Pushing Under Uncertain Dynamics

    Oct 4, 2025Aydin Ahmadi, Baris AkgunContact-Rich ManipulationNon-Prehensile Manipulation

    Computer Engineering Department, Ko¸c University, Rumelifeneri Yolu, Istanbul, 34450, Istanbul, Turkey.

  29. Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertion

    Sep 15, 2025Zongyao Yi, Joachim Hertzberg, Martin AtzmuellerGraph Neural NetworksContact-Rich Robotic Manipulation

    German Research Center for Artificial Intelligence, DFKI Niedersachsen, Osnabrück, Germany · Osnabrück University, Institute of Computer Science, Osnabrück, Germany

  30. Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation

    Apr 2, 2024Carlos Plou, Ana C. Murillo, Ruben Martinez-CantinModel-Based RLData-Efficient Robot Learning

    All authors are with the Instituto de Investigación en Ingeniería de Aragón (i3A) and DIIS, University of Zaragoza