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. Manipulation of Deformable Linear Objects Using Model Predictive Path Integral Control with Bidirectional Long Short-Term Memory Learning

    Sep 22, 2026Lukas Zeh, Johannes Meiwaldt, Zexu Zhou +2Deformable Object ManipulationRobot Dynamics Learning

    Institute for Control Engineering of Machine Tools and Manufacturing Units, University of Stuttgart, Stuttgart, Germany

  2. History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots

    Sep 22, 2026Hang Yang, Tingcong Liu, Junjie Xiong +2Flow MatchingRobot Dynamics Learning

  3. Robot World Models Are Not Invariant to How the Actions Are Written

    Sep 19, 2026Ahmed Karim, Leon ChlonWorld Models for RoboticsAction-Conditioned World Models

    Hassana Labs · University College London · University of Oxford

  4. Expert-Play Contouring Control: Faster-than-Demonstration Planning from Slow Expert and Fast Play

    Sep 19, 2026Seunghoon Cho, Wonsuhk Jung, Sundhar Vinodh Sangeetha +1Robot Motion PlanningRobotic Manipulation

    Seoul National University, Seoul, South Korea · Georgia Institute of Technology, Atlanta, GA, USA

  5. MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption

    Sep 17, 2026Donghyung Lee, Seyedali Golestaneh, Jaskrit Singh +3Non-Prehensile Robotic ManipulationRobotic Manipulation

  6. ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects

    Sep 16, 2026Tim Missal, Berk Guler, Lucas Domingues +3Bimanual Robotic ManipulationDeformable Object Manipulation

    Technical University of Darmstadt · Honda Research Institute Europe GmbH · School of Electrical and Computer Engineering, Universidade Estadual de Campinas (UNICAMP), Brazil +4

  7. Embedding Physics Priors in Robot Learning: A Survey

    Sep 15, 2026Mattia Piccinini, Lucas Schulze, Alice Plebe +10Robot LearningPhysics-Informed ML

    Professorship of Autonomous Vehicle Systems, Technical University of Munich, 85748 Garching, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI). · Department of Computer Science, Technical University of Darmstadt, Germany. · Department of Industrial Engineering, University of Trento, 38123 Trento, Italy. +1

  8. Orientation Control of Soft Robots via Adiabatic Spectral Submanifolds

    Sep 13, 2026Aron Karakai, Roshan S. Kaundinya, Mike Yan Michelis +2Robotic ControlSoft Robotics

    Automatic Control Laboratory, ETH Zurich, Switzerland · Institute for Mechanical Systems, ETH Zurich, Switzerland · Soft Robotics Laboratory and the ETH AI Center, ETH Zurich, Switzerland

  9. Identifying Habit, Physics, and Nuisance in Robot World Models

    Sep 6, 2026Jinting Hang, Zhenhui CaiWorld Models for RoboticsRobot Dynamics Learning

    Harvest Praxis

  10. Modular Deep Recurrent Neural Network: Application to Quadrotors

    Sep 3, 2026Nima Mohajerin, Steven L. WaslanderRecurrent Neural NetworksModular Neural Networks

    Dept. of Mechanical and Mechatronics Engineering University of Waterloo, Waterloo, ON,Canada

  11. Rapid On-Robot Learning for Dynamic Manipulation Skills: Robot Juggling

    Aug 27, 2026Taeyoon Lee, Chunpeng Wang, Christopher G. Atkeson +2Dynamic Robotic ManipulationContinual Robot Learning

    Robotics and AI Institute (RAI), Cambridge MA, USA · Robotics Institute, Carnegie Mellon University, Pittsburgh PA, USA

  12. Reward-Free Continual Adaptation for Resilient Space Robots

    Aug 24, 2026Andrej Orsula, Miguel Olivares-Mendez, Carol MartinezReinforcement LearningContinual Robot Learning

    University of Luxembourg

  13. NeSAM: Neuro-Symbolic Kinodynamics with Soil Adaptation for Off-Road Mobility

    Aug 21, 2026Chenhui Pan, Tong Xu, Francesco Cancelliere +1Terrain-Aware Robot LocomotionRobot Dynamics Learning

    Department of Computer Science, George Mason University, USA · Department of Electrical, Electronic and Computer Engineering, University of Catania, Italy

  14. Graph-Operator World Models for Morphology-Parameter Generalization in Continuous Control

    Aug 21, 2026Xu Yang, Yiqin Yang, Qianchuan ZhaoWorld Models for RoboticsModel-Based RL

    Tsinghua University · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences

  15. Learning the Right Abstraction: Neural Reduced Dynamics for Complex Robot Control

    Aug 19, 2026Harry Zhang, Dan NegrutRobotic ControlRobot Dynamics Learning

    Department of Mechanical Engineering, University of Wisconsin–Madison, Madison, 53706, WI, USA

  16. Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents

    Aug 17, 2026Zhijian Li, Chao Ren, Peijin Wang +1Collision AvoidanceWorld Model-Based Planning

    Aerospace Information Research Institute, Chinese Academy of Sciences, 100094 Beijing, China · School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China · University of Chinese Academy of Sciences, Beijing 100190, China +1

  17. SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning

    Jul 13, 2026Evelyn D'Elia, Weishu Zhan, Giulio Turrisi +5Reinforcement LearningKoopman Operator Learning

    Machine Learning and Optimisation, University of Manchester, M13 9PL Manchester, U.K. · Dynamic Legged Systems Lab, IIT, 16163 Genoa, Italy · Generative Bionics S.R.L, 16163 Genoa, Italy +3

  18. 3D Point World Models: Point Completion Enables More Accurate Dynamics Learning

    Jun 30, 2026Skand Peri, Hung Nguyen, Chanho Kim +2World Model LearningWorld Models for Robotics

    Oregon State University

  19. Continual Robot Policy Learning via Variational Neural Dynamics

    Jun 25, 2026Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4Robot Policy LearningRobot Policy Adaptation

    Robotics and Perception Group, University of Zurich

  20. Unified Motion-Action Modeling for Heterogeneous Robot Learning

    Jun 15, 2026Yunhao Cao, Shitong Liu, Chao Feng +4Robotic ControlRobot Dynamics Learning

    Cornell University

  21. Learning Context-Aware Neural ODE Dynamics for Adaptive Robotic Control

    Jun 13, 2026Shao-Yi Yu, Jen-Wei Wang, Maya Horii +2Robot Dynamics IdentificationRobot Dynamics Learning

    Department of Mechanical Engineering, University of California, Berkeley, CA, USA · Aalto University and YaiYai Ltd, Finland

  22. Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics

    Jun 8, 2026Rishabh Dev Yadav, Samaksh Ujjawal, Sihao Sun +2Robotic ControlRobot Dynamics Identification

    Department of Computer Science, The University of Manchaster, U.K. · Department of Cognitive Robotics, Delft University of Technology, Netherlands · International Institute of Information Technology Hyderabad. +1

  23. Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation

    Jun 1, 2026Zhiming Xu, Weitao Zhou, Xianghui Pan +4Robot Policy AdaptationLatent Dynamics Modeling

    School of Computer Science and Engineering, Tongji University, Shanghai, China · School of Vehicle and Mobility, Tsinghua University, Beijing, China · Simple AI, Beijing, China +1

  24. Batched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning

    May 29, 2026Yue Wang, Yanran Xu, Wenbo Wu +2Multibody DynamicsRobot Dynamics Learning

    University of Southampton, Southampton, SO17 1BJ, UK

  25. Dynamics Aware Quadrupedal Locomotion via Intrinsic Dynamics Head

    May 2, 2026Aman Arora, Nalini RathaRobot Dynamics LearningRL for Legged Locomotion

    Department of Computer Science, University at Buffalo, The State University of New York, Buffalo, USA

  26. Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots

    Apr 28, 2026Ziqing Zou, Ke Qiu, Fei Wang +3Gated Recurrent UnitRobot Trajectory Tracking

    Department of Control Science and Engineering, Zhejiang University, Hangzhou, China

  27. Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems

    Apr 21, 2026Wenjian Hao, Yuxuan Fang, Zehui Lu +1Robotic ControlReinforcement Learning

    School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA · independent researcher

  28. Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics

    Apr 15, 2026Angelo Moroncelli, Matteo Rufolo, Gunes Cagin Aydin +2Robot Dynamics IdentificationRobot System Identification

    University of Applied Science and Arts of Southern Switzerland, Department of Innovative Technologies, IDSIA-SUPSI, Lugano, Switzerland. · Università della Svizzera Italiana, Faculty of Informatics, Lugano, Switzerland. · Politecnico di Milano, Mechanical Department, Milano, Italy.

  29. Physics-Informed Reinforcement Learning of Spatial Density Velocity Potentials for Map-Free Racing

    Apr 10, 2026Shathushan Sivashangaran, Apoorva Khairnar, Sepideh Gohari +2Autonomous RacingRL Control

    Autonomous Robots and Vehicles Laboratory, College of Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA. · Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24061, USA. · Robotics Department, University of Michigan, Ann Arbor, MI 48109, USA.

  30. Morphology-Conditioned World Model for Cross-Embodiment Quadrupedal Locomotion

    Apr 9, 2026Mohamad H. Danesh, Chenhao Li, Amin Abyaneh +5World Models for RoboticsCross-Embodiment Robot Learning

    McGill University, Canada · Mila - Quebec AI Institute, Canada · ETH Zurich, Switzerland +1