Robot Dynamics Learning

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11 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 26

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OpenWAM: An Open Framework for Composable World-Action Models
Oct 6, 2026Heng Yu, David D. Yuan, Juze Zhang +7World Models for Robotics Robot Dynamics Learning
Stanford University
AIM: Adaptive Interaction Modeling Networks for Real-to-Sim Soft-Body Simulation
Oct 5, 2026Tiancheng Yang, Dingshuo Chen, Tianle Chen +2Robotics Simulation Robot Dynamics Learning
NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences +1
Optimal Control with Learned Critics under Unmodeled State Dependencies
Oct 4, 2026Philipp Schoch, Markus RyllResidual Dynamics Learning Robot Dynamics Learning
Autonomous Aerial Systems Lab Technical University of Munich
Learning a Resolution-Consistent Jacobian Field for Bio-Inspired Rigid-Soft Finger
Oct 1, 2026Tianyou Liang, Haisen Zeng, Shanjun Chen +3Robot Dynamics Learning Soft Robotics
College of Intelligence Science and Technology, National University of Defense Technology,, Changsha, 410073, China · National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology,, Changsha, 410073, China
EIDA: Execution-Interface Dynamics Adaptation for Real-to-Sim-to-Real Robot Navigation
Oct 1, 2026Yiwei Qian, Shanze Wang, Qingyuan Hu +2Robot Dynamics Identification Robotics Simulation
Eastern Institute of Technology, Ningbo, China · National University of Singapore, Singapore · Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong +1
WorldLine: Action-Driven Visual Simulation for Robotic Manipulation
Sep 29, 2026Shenghe Zheng, Wenbo Li, Jiyao Zhang +4Action-Conditioned Video Generation World Models for Robotics
The Hong Kong University of Science and Technology · Joy Future Academy · Peking University +1
Scouting the Dynamics Gap: Test-Time Policy Adaptation via Action-Outcome Feedback
Sep 28, 2026Yishu Li, Liyuan Geng, Xinyi Mao +2Robot Policy Learning Test-Time Adaptation
Robotics Institute, Carnegie Mellon University · Computer Science and Technology, Tsinghua University
EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
Sep 28, 2026Yichao Liang, Amber Li, Dat Nguyen +13World Models for Robotics Residual Dynamics Learning
Basis Research Institute · University of Cambridge · Carnegie Mellon University +6
From World Models to World Action Models: Rethinking Next-State Prediction
Sep 28, 2026Tingyu Yuan, Ziming Ji, Biaoliang Guan +9Robot Dynamics Learning Action-Conditioned World Models
CASIA · UCAS · BUPT +4
AquaWAM: A Dynamics-aware World Action Model for Underwater Embodied Agents
Sep 27, 2026Cunhao Zhu, Yifeng Wang, Dongliang Xu +3Robot Dynamics Learning Action-Conditioned World Models
Shandong University (sdu.edu.cn) · Beijing Institute of Technology (bit.edu.cn)
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 Manipulation Robot Dynamics Learning
Institute for Control Engineering of Machine Tools and Manufacturing Units, University of Stuttgart, Stuttgart, Germany
History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots
Sep 22, 2026Hang Yang, Tingcong Liu, Junjie Xiong +2Flow Matching Robot Dynamics Learning
ForwardDLO: Model-Based Bimanual Shape Matching of Unconstrained Deformable Linear Objects
Sep 16, 2026Tim Missal, Berk Guler, Lucas Domingues +3Bimanual Robotic Manipulation Deformable Object Manipulation
Technical University of Darmstadt · Honda Research Institute Europe GmbH · School of Electrical and Computer Engineering, Universidade Estadual de Campinas (UNICAMP), Brazil +4
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 Learning Koopman 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
3D Point World Models: Point Completion Enables More Accurate Dynamics Learning
Jun 30, 2026Skand Peri, Hung Nguyen, Chanho Kim +2World Model Learning World Models for Robotics
Oregon State University
Continual Robot Policy Learning via Variational Neural Dynamics
Jun 25, 2026Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren +4Robot Policy Learning Robot Policy Adaptation
Robotics and Perception Group, University of Zurich
Unified Motion-Action Modeling for Heterogeneous Robot Learning
Jun 15, 2026Yunhao Cao, Shitong Liu, Chao Feng +4Robotic Control Robot Dynamics Learning
Cornell University
Learning Context-Aware Neural ODE Dynamics for Adaptive Robotic Control
Jun 13, 2026Shao-Yi Yu, Jen-Wei Wang, Maya Horii +2Robot Dynamics Identification Robot Dynamics Learning
Department of Mechanical Engineering, University of California, Berkeley, CA, USA · Aalto University and YaiYai Ltd, Finland
Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
Jun 8, 2026Rishabh Dev Yadav, Samaksh Ujjawal, Sihao Sun +2Robotic Control Robot 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
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 Adaptation Latent 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
Batched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning
May 29, 2026Yue Wang, Yanran Xu, Wenbo Wu +2Multibody Dynamics Robot Dynamics Learning
University of Southampton, Southampton, SO17 1BJ, UK
Dynamics Aware Quadrupedal Locomotion via Intrinsic Dynamics Head
May 2, 2026Aman Arora, Nalini RathaRobot Dynamics Learning RL for Legged Locomotion
Department of Computer Science, University at Buffalo, The State University of New York, Buffalo, USA
Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots
Apr 28, 2026Ziqing Zou, Ke Qiu, Fei Wang +3Robot Trajectory Tracking Robot Dynamics Learning
Department of Control Science and Engineering, Zhejiang University, Hangzhou, China
Efficient Reinforcement Learning using Linear Koopman Dynamics for Nonlinear Robotic Systems
Apr 21, 2026Wenjian Hao, Yuxuan Fang, Zehui Lu +1Robotic Control Reinforcement Learning
School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN 47907, USA · independent researcher
Model-Based Adaptive Precision Control for Tabletop Planar Pushing Under Uncertain Dynamics
Oct 4, 2025Aydin Ahmadi, Baris AkgunContact-Rich Manipulation Non-Prehensile Manipulation
Computer Engineering Department, Ko¸c University, Rumelifeneri Yolu, Istanbul, 34450, Istanbul, Turkey.
Learning Contact Dynamics through Touching: Action-conditional Graph Neural Networks for Robotic Peg Insertion
Sep 15, 2025Zongyao Yi, Joachim Hertzberg, Martin AtzmuellerGraph Neural Networks Contact-Rich Robotic Manipulation
German Research Center for Artificial Intelligence, DFKI Niedersachsen, Osnabrück, Germany · Osnabrück University, Institute of Computer Science, Osnabrück, Germany

Oct 6, 2026, cs.ROJ/K move · Enter open · S save

OpenWAM: An Open Framework for Composable World-Action Models

Heng Yu, David D. Yuan, Juze Zhang, Changan Chen+6

Stanford University

World Models for Robotics Robot Dynamics Learning World Action Models Robot Learning

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World-action models (WAMs) couple future prediction with robot control, yet existing systems often vary the video backbone, interaction structure, supervision, and inference procedure simultaneously, making their design choices difficult to compare. We introduce OPENWAM, an open world-action modeling framework built around a common causal robot-video foundation and configurable video-action interaction. Starting from Wan2.2-5B, we perform causal robot-video pretraining on over 10,000 hours of video, then integrate an action expert through a shared Mixture-of-Transformers architecture that supports joint, video-then-action, action-then-video, and decoupled generation. OPENWAM achieves high success rates on four LIBERO suites and real-world bimanual tasks; robot-video training with causal adaptation improves VTA success on LIBERO-Long from 68.4% to 97.8%. The same configurable architecture naturally extends to inverse and forward dynamics, allowing us to study how counterfactual transitions improve independently trained dynamics models beyond demonstrations alone. When only the video predictor is adapted to a new task, a frozen local-context inverse dynamics model trained on counterfactual data and demonstrations achieves 84.0% mean success across four held-out LIBERO-90 tasks, compared with 47.0% for a full-context inverse model and 21.5% for a local-context model trained only on demonstrations. For forward dynamics, counterfactual supervision reduces RGB prediction error by 34.5% and raises outcome identification from 21.1% to 71.3% among 16 same-state outcomes. OPENWAM provides a common testbed for comparing WAM interaction designs and for studying dynamics learning from video data beyond successful demonstrations.

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