Robot Dynamics Identification

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6 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 31

Oct 1, 2026cs.RO

EIDA: Execution-Interface Dynamics Adaptation for Real-to-Sim-to-Real Robot Navigation

Simulation-to-robot transfer can fail when velocity commands produce motion and feedback that differ from those modeled during policy training. We present execution-interface dynamics adaptation (EIDA), which fits these responses from target-platform execution data without reconstructing actuator dynamics. A model of body-frame pose increments updates simulator geometry, while a separate model predicts the velocity feedback observed by the policy; a short history of velocity feedback is included in the policy input. The fitted models are used within a lightweight GPU-parallel simulator. On the full Jackal and Go2 validation sets, the fitted models reduced position and yaw prediction errors relative to the simulator's predefined motion model. Across 100 benchmark navigation environments evaluated in a separate physics-based simulator, EIDA achieved the highest success rate and navigation score among the compared learned policies, both with and without global guidance. Feedback ablations further supported the need to match policy-facing velocity estimates. On a physical Unitree Go2, EIDA reached the goal without collision in all 20 static-scene trials, compared with 4 of 20 for the baseline. These results show that execution-interface adaptation can improve navigation transfer without detailed actuator simulation.

Yiwei Qian, Shanze Wang, Qingyuan Hu +2

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

Robot Dynamics Identification Robotics Simulation Robot Navigation Sim-to-Real Transfer Robot Dynamics Learning

Sep 28, 2026cs.RO

On the Numerical Reliability of Differentiable Physics-Based Optimization for Robotic Material Manipulation

Differentiable physics is increasingly used in robotic material manipulation for system identification, trajectory or skill optimization, demonstration generation, and robot or end-effector design. These applications depend on gradients propagated through long, contact-rich simulation rollouts. We study the numerical reliability of those gradients using two Material Point Method (MPM) system-identification benchmarks derived from elastoplastic and granular manipulation. The benchmarks provide controlled cases for three effects that also arise in broader differentiable physics-based optimization. GPU many-to-one sums whose order depends on thread scheduling changed long-horizon gradients and reversed the sign of one parameter gradient relative to a deterministic reference. Finite-difference checks became less reliable for longer rollouts because repeated-run loss variation grew much faster than the loss change produced by the tested parameter perturbations. Observation and loss definitions changed optimization behaviour and the solution preferred by an independent metric. These results motivate reproducible accumulation, finite-difference validation that compares perturbation-induced loss changes with repeated-run variation, and explicit reporting of objective construction when differentiable simulation is used for robotic optimization.

Xintong Yang, Minglun Wei, Yu-Kun Lai +1

School of Engineering, Cardiff University, Cardiff, United Kingdom · School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom · College of Mechanical and Electrical Engineering, Hohai University, China

Robot Dynamics Identification Material Point Method Robotic Manipulation Differentiable Physics Differentiable Optimization

Sep 15, 2026cs.RO

Adaptive-MHE : A Sampling-Based Adaptive MPC for Legged Loco-Manipulation via Moving Horizon Estimation

Legged robots have demonstrated a remarkable ability to traverse various terrains, yet generating effective loco-manipulation behaviors remains challenging. A key difficulty is that object and terrain parameters are typically unknown to the robot, and mismatches between these parameters and their simulated counterparts introduce a sim-to-real gap that degrades control performance. Classical system identification (Sys-ID) methods often assume differentiable dynamics, an assumption that does not hold for contact-rich legged systems. Sampling-based Sys-ID avoids this restriction by directly matching simulated and recorded state trajectories through massively parallel rollouts, but existing approaches are typically applied offline and do not adapt as environmental conditions change. We present Adaptive-MHE an online sampling-based Sys-ID framework, based on moving horizon estimation (MHE), that estimates the physical parameters of objects and terrain in the environment (e.g., mass, friction) and couples this estimate with a sampling-based model predictive controller, enabling adaptive loco-manipulation in changing and uncertain environments. In simulation and hardware experiments, our framework consistently outperforms baselines and matches the performance of a controller with access to ground-truth parameters.

Hossein Keshavarz, Alejandro Ramirez-Serrano, Majid Khadiv

University of Calgary, Department of Mechanical Engineering, 2500 University Drive NW, Calgary, AB, Canada · Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany

Robot Dynamics Identification Legged Robotics Model Predictive Control System Identification Robot State Estimation

Sep 15, 2026cs.RO

Port-Hamiltonian Koopman Operator Synthesis for Mechanical Systems

Finite-dimensional Koopman models enable efficient linear prediction and control of nonlinear robotic systems. However, models learned purely from trajectory data may violate the energetic structure of the underlying mechanics, producing predictions that exhibit artificial energy growth and diverge under recursive propagation. This work presents a structure-preserving Koopman framework for Euler-Lagrange systems built on generalized-momentum coordinates. The momentum transformation exposes the mechanical actuation as a known, state-independent port, which is preserved explicitly in the lifted dynamics. A structure-constrained neural architecture is developed to jointly learn the lifting functions and a port-Hamiltonian Koopman generator, rendering the learned dynamics passive by construction rather than through penalty terms or post-hoc projection. A Cayley-midpoint discretization further preserves the corresponding storage-dissipation balance exactly in discrete time. These properties are established analytically by deriving the discrete storage balance and associated stability guarantees of the learned predictor. Simulation and experimental studies demonstrate improved prediction accuracy, data efficiency, and closed-loop tracking over Koopman baselines, with increasing gains for higher-dimensional systems.

Rajpal Singh, Aditya Singh, Jishnu Keshavan

Department of Mechanical Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India

Robot Dynamics Identification Koopman Operator Learning Nonlinear System Identification Port-Hamiltonian Systems

Sep 15, 2026cs.RO

Optimal Excitation Trajectories for System Identification of Underwater Vehicles

In this work, we propose a structured methodology for the system identification of underwater vehicles through the design of optimal excitation trajectories. To this end, the trajectories are parameterized using Bezier curves, which ensure smooth and differentiable motion profiles while facilitating the enforcement of constraints through appropriate manipulation of the control points. An optimization problem is formulated to determine a dynamically feasible excitation trajectory that respects safety limits and maximizes the quality of the collected data, thereby enabling reliable estimation of the vehicle's dynamic parameters using least squares. The proposed methodology is experimentally validated in a laboratory water tank, where the dynamic parameters, identified from the optimized trajectory, are evaluated by predicting the vehicle's velocity through forward simulation on previously unseen trajectories.

Fotis Panetsos, Kostas J. Kyriakopoulos

Center for AI & Robotics (CAIR) and the Electrical Eng. Program, Engineering Division, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.

Robot Dynamics Identification Trajectory Optimization Vehicle Dynamics Underwater Robotics

Sep 8, 2026cs.RO

Model Predictive Control of Tensegrity Robots via Contact-Aware Graph Neural Dynamics Model

Tensegrity robots offer lightweight, compliant mobility over challenging terrain but remain difficult to model and control due to complex contact-rich dynamics and partial observability. This work presents a model predictive path integral (MPPI) controller for a three-bar tensegrity robot driven by a learned graph neural network (GNN) dynamics model. This work first extends prior GNN-based models with a differentiable contact detection module. The extension allows the dynamics model to reason over non-horizontal planar terrains, obstacles, as well as self-collisions. Then, the learned dynamics model and the MPPI controller operate in a closed data-collection loop, iteratively improving model accuracy and control performance. This work further introduces a hybrid MPPI strategy that combines MPPI with turning motion primitives to improve maneuverability. Experiments are performed in MuJoCo across five navigation tasks, which include, wall obstacles, inclines, narrow corridors, low-clearance structures, and a composite 3D obstacle course. The experiments demonstrate that the hybrid MPPI controller operating over the learned GNN dynamics model improves predictive accuracy over a flat-ground baseline model and achieves superior navigation performance compared to

A^*

-based re-planning and MPPI-only variants. Results show that the contact-aware learned dynamics combined with the sampling-based model predictive control enable robust tensegrity navigation in complex, contact-rich environments.

Nelson Chen, Patrick Meng, Charles Tang +5

Computer Science, Rutgers University, Piscataway, NJ, USA. · Mechanical Engineering, Yale University, New Haven, CT, USA.

Robotic Control Robot Dynamics Identification MPPI Control Robot Motion Planning

Aug 31, 2026cs.RO

A Hybrid PEM-GP Framework for Uncertainty-Aware System Identification of Quadcopters

Accurate dynamic models play a central role in achieving reliable control of quadcopters. Classical system identification methods remain widely used, mainly because of their interpretability. However, they often fail to capture important nonlinear effects, especially in small-scale aerial platforms where such effects become more pronounced. Data-driven approaches offer a different perspective. They can represent complex nonlinear dynamics more effectively, but this comes at the cost of reduced interpretability and the absence of well-calibrated uncertainty estimates. In this work, we propose a framework that combines physics-based modeling with data-driven learning, while explicitly accounting for uncertainty. A physics-based model is first identified using the Prediction Error Method (PEM), which captures the main structure of the system. The remaining dynamics are then modeled using a Gaussian Process (GP), allowing the residual behavior to be learned directly from data. This separation makes it possible to distinguish between known physical effects and unmodeled dynamics. The proposed framework is validated on a Duckiedrone-like experimental setup. The results show that the PEM-GP model achieves prediction accuracy comparable to that of a Long Short-Term Memory (LSTM) network, while additionally providing calibrated uncertainty estimates. This combination improves model reliability and supports uncertainty-aware decision-making.

Abdallah Ghoul, Ismail Khalil Bousserhane, Kadri Boufeldja

Electrical Engineering Department, University of Bechar, Bechar 08000, Algeria

Robot Dynamics Identification Nonlinear System Identification Physics-Informed ML Uncertainty Calibration

Aug 8, 2026cs.RO

Stochastic Physics-Informed Neural Networks on Lie Groups for Learning Underwater Vehicle Dynamics

Accurate models of underwater vehicle motion are needed for autonomous execution of marine tasks like infrastructure inspection and scientific sampling. However, such motion is challenging to characterize using traditional physics-based methods. This paper presents a novel data-driven framework for learning stochastic underwater vehicle dynamics. Using Euler-Poincaré dynamics and the geometry of Lie groups, we develop a stochastic physics-informed neural network architecture that respects the physical and geometric constraints of underwater vehicles. Our approach leverages structure-preserving stochastic integration and builds upon moment matching and finite dimensional matching to ensure geometrically-consistent training. We evaluate our approach in simulation and on an underwater vehicle navigating dock pylons in a harbor environment. The results demonstrate that our method learns accurate and robust dynamics models, enabling safe model-based control in challenging marine environments.

Evan F. Palmer, Ross L. Hatton, Geoffrey A. Hollinger

Collaborative Robotics and Intelligent Systems (CoRIS) Institute, Oregon State University, Corvallis OR 97331, USA

Robot Dynamics Identification Underwater Robotics System Identification Physics-Informed ML

Aug 2, 2026cs.RO

Rapid Embodiment Adaptation for Quadrupedal Locomotion

Humans readily adapt their movements as their bodies change through aging, injury, or load carrying, but learning-based robot policies often break when hardware properties shift. We introduce an online embodiment adaptation framework for quadrupedal locomotion that infers embodiment parameters from short interaction histories and conditions control on the inferred hardware state. Our method pairs a generalist policy trained under embodiment randomization with a lightweight adaptation module that identifies physical changes within half a second. We evaluate two representative forms of embodiment variation: joint-range constraints and trunk-mass changes, corresponding to joint-level kinematic degradation and body-level dynamic variation. In simulation, the module accurately estimates these changes and enables closed-loop control that substantially outperforms policies conditioned directly on interaction history. On a real Unitree Go2 robot, our system maintains stable locomotion under severe instances of the evaluated changes, including a fully locked leg and a 5 kg payload, where non-adaptive methods fail. These results demonstrate the practicality of explicit online embodiment identification for rapid adaptation to joint-limit and payload-mass changes, and provide a step toward handling broader forms of uncertain, degraded, or changing robot hardware.

Dichen Li, Bo Ai, Nico Bohlinger +3

UC San Diego · Stanford University · TU Darmstadt +2

Robot Dynamics Identification Robot Policy Adaptation RL for Legged Locomotion Quadruped Locomotion

Jul 25, 2026cs.RO

Sling2Sim2Real: One-Shot Elastic System Identification for Non-Destructive Slingshot Policy Learning

Elastic object manipulation (EOM) involves highdimensional, nonlinear, and elastic deformations. The diverse deformation properties of elastic objects substantially expand the relevant state space, requiring extensive exploration to learn accurate manipulation policies for tasks such as slingshot manipulation. While simulation enables large-scale and safe exploration compared to costly and potentially destructive real-world trials (e.g., repeated projectile launches), accurately calibrating elastic behavior between the real world and simulation remains challenging since elastic properties are largely indistinguishable from visual observations alone. To address these challenges, we propose Sling2Sim2Real, a one-shot Real2Sim2Real framework that identifies elastic parameters from a single non-destructive interaction and enables policy learning in simulation. The framework consists of two stages: 1) a multi-start Real2Sim system identification method that exploits parameter covariance to estimate elastic properties, and 2) simulation-based policy learning followed by zero-shot Sim2Real transfer using the calibrated simulator. We evaluate Sling2Sim2Real on a slingshot manipulation task using a Franka Emika Panda arm and elastic bands with diverse physical properties across varying target distances. Experimental results demonstrate that Sling2Sim2Real achieves accurate policy learning and robust generalization while significantly reducing the amount of required real-world interaction.

Wonjae Kang, Geonwoo Kim, Minseok Song +1

School of Computing, Korea Advanced Institute of Science and Technology, Korea

Robot Dynamics Identification Robotics Simulation Sim-to-Real Transfer Deformable Object Manipulation System Identification Deformable Object Simulation

Jul 20, 2026cs.RO

World Translation: Minimizing Sim-to-Real Gap with Backward Dynamics Extraction and Unpaired Domain Translation

The gap between simulation and reality remains a fundamental challenge in deploying simulation-trained robotic policies in the real world. Real-to-sim methods narrow this gap from the real side, learning transition dynamics from real data to build a more realistic digital world. Learned dynamics models are their dominant instance. Such methods, however, face a partial observability problem: the same observation may branch to different transitions due to unobservable factors. Existing methods assume these factors can be recovered from observation history. However, this may fail whenever observation history is uninformative, such as a sudden contact event with no prior warning. To address this limitation, we propose \textit{World Translation}, which exploits a complementary strength of simulators and learned dynamics. Simulators are deterministic but physically imperfect, while learned models are accurate but underdetermined under partial observability. Rather than predicting transitions forward from history, we extract the unobservable dynamics information backward from an observed transition, then translate this feature across simulation and reality as an unpaired domain-translation problem that preserves dynamics content while transferring domain style. Experiments across humanoid, quadruped, and manipulator platforms show that our method achieves more accurate dynamics modeling than baselines, with the largest gains when unobservable factors cannot be recovered from observation history. Real-robot deployment on Go2 quadruped confirms improved policy transfer.

Xinchen Yao, Leixin Chang, Hua Chen

Zhejiang University · LimX Dynamics

Robot Dynamics Identification Synthetic-to-Real Domain Adaptation Sim-to-Real Transfer

Jul 15, 2026cs.RO

Design, Modeling and Experimental Validation of a Miniature Hybrid Underwater Glider With Large-Range Foldable Deflectable Wings

Miniature hybrid underwater gliders have attracted increasing attention for long-endurance ocean observation and confined-space inspection. Large-range wing reconfiguration offers a promising yet largely unexplored approach for simultaneously enhancing maneuverability and shape adaptability in constrained underwater environments. However, such morphing introduces substantial challenges in mechanical integration, dynamic modeling, and hydrodynamic characterization. This paper presents FoDeGlider, a miniature hybrid underwater glider equipped with two independently actuated wings capable of large-range folding and deflection. To capture configuration-dependent variations in mass distribution, center-of-geometry location, and hydrodynamic loading, a multibody dynamics model is developed by treating wing configuration as a structural variable. A composite rigid body algorithm (CRBA)-based projection formulates the composite inertia, wrench transformations, and component-level hydrodynamics into a unified Fossen-form dynamic model applicable to arbitrary wing configurations. A sequential parameter-identification framework is further proposed to estimate fuselage and wing hydrodynamic coefficients, resulting in an open benchmark dataset for model identification and validation. Extensive experiments are conducted, the results of which demonstrate accurate dynamic modeling and parameter identification across diverse morphing configurations. Gate traversal experiments further validate FoDeGlider's ability to actively reconfigure its morphology during locomotion, enabling enhanced navigation in confined underwater environments.

Yongjian Zhu, Yusen Tao, Feitian Zhang

Robotics and Control Laboratory, School of Advanced Manufacturing and Robotics, and the State Key Laboratory of Turbulence and Complex Systems, Peking University, Beijing 100871, China

Robot Dynamics Identification Underwater Robotics

Jul 14, 2026cs.LG

Learning Forced Multibody Dynamics on Lie Groups

We propose an architecture for learning the dynamics of mechanical systems based on discrete forced Euler-Lagrange equations on Lie groups using only position data. By formulating the dynamics directly on manifold-valued configuration spaces, the method naturally respects the geometric structure of the systems and preserves geometric invariants and conservation laws. The reliance on position measurements alone makes the framework applicable in settings where velocity data are unavailable or noisy. The approach extends naturally to multibody systems, accommodates external control inputs, and demonstrates strong performance on both synthetic and real-world datasets.

Martine Dyring Hansen, Marta Ghirardelli, Elena Celledoni +2

Department of Mathematics and Cybernetics, SINTEF Digital Forskningsveien 1, 0373 Oslo, Norway · Department of Mathematical Sciences Norwegian University of Science and Technology (NTNU) Alfred Getz’ vei 1, 7034 Trondheim, Norway · Instituto de Ciencias Matematicas (CSIC) Calle Nicolás Cabrera 13-15, Cantoblanco, 28048 Madrid, Spain

Robot Dynamics Identification Dynamical Systems Multibody Dynamics Nonlinear System Identification Lie Group Methods

Jul 13, 2026cs.RO

NeuralActuator: Neural Actuation Modeling for Robot Dynamics and External Force Perception

Differentiable simulators have advanced policy learning and model-based control, yet actuator dynamics remain an important source of sim-to-real error. This is particularly acute on low-cost platforms, where the linear current-to-torque relation

τ= K_tI

becomes unreliable during commanded-target tracking because of friction, hysteresis, backlash, and thermal effects. We present NeuralActuator, a neural actuator model that jointly predicts (i) a simulator-equivalent generalized-effort surrogate for trajectory propagation on low-cost servo platforms, (ii) external force with a contact-probability gate for sensorless force perception, and (iii) a motor-condition score for the supervised joint. We also introduce the Neural Actuation Dataset (NAD), collected with a twin-arm teleoperation system that records robot states and actuator telemetry together with external-force labels. The torque-surrogate head is trained through differentiable simulation from pose trajectories without direct generalized-effort labels, while the force, gate, and motor-condition heads receive direct supervision. A Transformer captures temporal dependencies while supporting real-time inference. We evaluate NeuralActuator on a 5-DoF OpenManipulator-X, a 6-DoF SO-101, and a 7-DoF Franka Emika Panda, spanning three actuator families and platforms costing approximately USD 500 to over USD 30,000. The low-cost platforms support dynamics and force evaluation, while the offline Franka experiment provides an additional payload-force-estimation benchmark. Experiments further demonstrate its application for motor condition estimation on OpenManipulator-X and improved behavior-cloning performance when NeuralActuator is used as a pretrained module.

Zhiyang Dou, John U. Onyemelukwe, Hangxing Zhang +9

1MIT · 2Amazon Robotics

Neural Surrogate Modeling Robot Dynamics Identification Robotics

Jul 6, 2026cs.RO

Efficient Transfer Learning of Robot Dynamic Models Using Morphological Similarity

This study proposes a neural network-based transfer learning framework for modeling the dynamics of soft, fin-actuated underwater robots. We focus on morphologically similar robots that differ in scale and hydrodynamic properties. A model trained on data from a larger robot (source domain) is adapted to a smaller one (target domain) with limited labeled data. To enable label-efficient transfer, we develop an autoencoder-based domain adaptation approach that learns a shared latent representation aligning the dynamics of both robots. Experiments on two real underwater robots show that the proposed method enables accurate state estimation of the body-frame velocities on a target platform without labeled data, highlighting its potential for efficient cross-robot dynamics transfer among morphologically similar platforms.

Pavlo Kupyn, Yuya Hamamatsu, Roza Gkliva +2

Department of Computer Systems, Tallinn University of Technology, Tallinn, Estonia

Robot Dynamics Identification Unsupervised Domain Adaptation Cross-Embodiment Robot Learning Robotics Underwater Robotics Transfer Learning+1

Jul 1, 2026cs.RO

[Preprint] Dynamic Modeling, Gait Synthesis, and Control of a Novel Subsurface Bore Propagator

In this article, we present dynamic modeling, gait synthesis, and feedback control design for a modular novel subsurface robot, designed for human-free subsurface exploration and excavation. The subsurface propagator design is based on two major aspects: 1) anchor and propel movement like an earthworm and 2) excavation similar to tunnel boring machines. This design is decoupled into five separate modules: one drill head to excavate and create cavity for propagation, two modules to anchor the robot, and two modules to enable propagation of the body. In order to design a controller for each of the modules, dynamic models using the Euler-Lagrange framework are developed. These mathematical models are used as a baseline to design controlled decoupled operation of the different joint movements. The operation of robotic assembly is constructed via a centralized state machine for gait synthesis with integration of the designed feedback controller. The controllers are tested on the real robot geometry to aid sim-to-real integration: A physics-based Unity simulation using a CAD model of the robot and integration of the trained controller via ROS verifies the performance of the robot. The experimental results demonstrate that the proposed design, controllers and the gait synthesis strategy together are capable of anchoring the robot in place and creating an total advancement of 30,mm into the soil after completing 3 gait cycles.

Lina van Brügge, Shruti Kotpalliwar, Anton Koval +2

Robotic Control Robot Dynamics Identification Robotics Robot Motion Generation Robot Locomotion

Jun 27, 2026cs.RO

Physics Models for Sim-to-Real Transfer in Professional-Level Robot Table Tennis

At competitive speeds and spins, a table tennis ball follows complex, counterintuitive trajectories that a robot must track and precisely counter within fractions of a second. Training a reinforcement learning policy capable of these skills is prohibitively expensive and dangerous in the real world, making high-fidelity simulation essential. Transferability of such policies, however, critically depends on how faithfully the simulation captures real-world dynamics - a requirement made even more stringent by the adversarial nature of the game, where any modeling inaccuracy becomes an exploitable weakness for the opponent. Prior state-of-the-art in robot table tennis generally focuses on a limited range of velocities and spins and fails to capture the richness of ball behaviors encountered in professional-level play. In this work, we present physics models for aerodynamic ball flight, ball-table contact, and ball-racket contact. that accurately capture the ball behavior over a vast range of speeds and spins relevant to the game. Specifically, we model drag and Magnus force coefficients as functions of Reynolds number and spin ratio in the aerodynamics equations. For the table contact model we model effects of ball buckling on the coefficient of restitution and incorporate residuals into the instantaneous point-contact models. For the racket contact model, we introduce a residual neural network component to complement coefficients related to normal and tangential coefficients of restitution as well as torsional spin damping. Evaluated on an unprecedentedly large dataset of competitive matches (277 games), the proposed models significantly reduces prediction errors (e.g., 59% median landing-position error reduction). The resulting models were used to train the RL policies for the first real-world robot table tennis AI agent capable of competing against professional players.

Christian Conti, Bilan Yang, Alexander Sigrist +4

Sony AI, Tokyo, Japan · Sony AI, Zurich, Switzerland · Sony AI, Zürich, Switzerland

Robot Dynamics Identification Robotics Simulation Sim-to-Real Transfer Robotics

Jun 25, 2026cs.RO

Hardware Design for Table Tennis Robot Capable of Beating Professional Players

This paper focuses on the hardware specifications required for a table tennis robot to beat professional players. After analyzing the motions of elite players, we defined target specifications for the workspace, payload, external-force resistance, physical performance, serve capability, and end-effector accuracy. Based on these specifications, we developed "Ace", a custom 8-DoF robot. The mechanical structure was improved through topology optimization to minimize mass while preserving stiffness. Motor and gearbox selection was optimized using an inverse-dynamics torque model. Low-order per-joint dynamics models with delay compensation were identified and integrated into simulation to enable the use of an RL control policy. Experiments demonstrated repeated full-stroke swings with a cycle time of 0.8 s and a peak racket-center velocity of 22 m/s. The robot successfully defeated multiple professional players.

Nobuhiko Mukai, Pavel Adodin, Stefan Heusser +6

Sony AI in Tokyo, Japan · Sony AI in Zürich, Switzerland · Sony Global Manufacturing & Operations in Tokyo, Japan

Robot Dynamics Identification Topology Optimization Robotics

Jun 23, 2026cs.RO

RigPI: Dynamic Parameter Identification of Rigid Body via VLM-Seeded Differentiable Simulation

Accurate physical parameter identification of manipulated objects is fundamental to advanced robotic manipulation and the construction of faithful digital twins. However, acquiring physically consistent inertial and frictional properties from real-world interactions remains challenging due to sensing noise, modeling errors, and limited prior knowledge. This paper presents RigPI, a systematic framework for identifying dynamic parameters of both unconstrained rigid bodies and multi-link rigid bodies during robot-object interaction. RigPI integrates vision-based semantic priors, force-torque measurements, and motion observations within a differentiable simulation pipeline. A vision-language model (VLM) provides informed initialization and a constrained search space, while gradient information from a differentiable physics simulator enables efficient and stable parameter refinement. The proposed two-stage optimization strategy alleviates sensitivity to noise and avoids physically implausible solutions. Extensive real-world experiments on objects with revolute and prismatic joints demonstrate that RigPI achieves accurate and stable parameter estimates, and successfully reproduces manipulation trajectories on a real robot with parameter-aware predictive validity. These results highlight the effectiveness and robustness of RigPI for real-world robotic system identification tasks.

Xincheng He, Rongrong Zhang, Wei Jiang +1

Way Robotics

Robot Dynamics Identification Robotic Manipulation System Identification

Jun 20, 2026cs.RO

How Should a Simulation-to-Reality Transfer Budget Be Spent?

Simulation-to-reality transfer, often called sim-to-real transfer, is a central challenge in robot learning. Yet, the tradeoff between measuring a system more accurately and training over a broader range of simulated dynamics is still poorly understood. In this work, we focused on the allocation of real-robot measurement time between system identification and domain randomization. We studied this tradeoff in a controlled sim-to-sim pendulum setting, where a hidden-parameter model stands in for the physical robot, and the experiment sweeps identification rollouts against the width of the randomization distribution. Across the reality gaps and noise levels we tested, the measurement budget did most of the work. A small number of identification rollouts closed most of the transfer gap, and once any real data was available, policies performed best when trained at the estimated parameters rather than over a widened randomization band. Broad randomization that contained the true system still did not substitute for measurement. These results hold in a benign regime where the dynamics are identifiable and only two parameters are unknown, so structural model mismatch remains the setting where randomization breadth may become more valuable. Overall, our results suggest that sim-to-real pipelines should first measure the parameters they can and reserve randomization for the uncertainty that remains.

Syed Hamzah Rizvi, Yash Vardhan Tomar

Purdue University West Lafayette, USA

Robot Dynamics Identification Domain Randomization Sim-to-Real Transfer System Identification Robot Learning

Jun 15, 2026cs.RO

Reinforcement Learning with Inner-loop Dynamics Estimator for Aerial Manipulation under Uncertainty

Aerial manipulators enable physical interaction in hard-to-reach environments; however, the combined problem of direct whole-body aerial manipulation under rapid arm motion, payload changes, and related unknown dynamic uncertainty remains a largely unsolved problem. We present a hierarchical control framework that combines Reinforcement Learning (RL) with an inner-loop dynamics estimator to address this problem. The RL outer loop maps desired 6-degrees-of-freedom (DOF) end-effector targets to coordinated whole-body commands, enabling direct task-driven control without relying on a fully accurate coupled dynamic model in the policy layer. An inner loop then tracks these commands while compensating for transient inertial shifts and uncertainty during execution via a dynamics estimator scheme without requiring system model knowledge. We validate the proposed approach on a custom quadrotor equipped with a 3-DoF manipulator through hardware experiments under varying payload conditions. Compared with RL+PID and RL+INDI+PID baselines, the proposed method reduces end-effector tracking error and improves task success rate across the tested hardware conditions. These results show that combining learned whole-body coordination with estimator-based low-level compensation improves the precision and robustness of aerial manipulation under changing operating conditions.

Shivansh Pratap Singh, Samaksh Ujjwal, Ishita Chaudhary +3

International Institute of Information Technology Hyderabad, India. · Department of Computer Science, University of Manchester, UK.

Robot Dynamics Identification RL for Robotics Quadrotor Control Aerial Manipulation

Jun 13, 2026cs.RO

Learning Context-Aware Neural ODE Dynamics for Adaptive Robotic Control

Robotic systems deployed in uncertain and dynamically changing environments often face variations in contact conditions, aerodynamic effects, and external disturbances that challenge reliable control. To remain effective under model-based control, these systems require dynamics models that can adapt to such changes, especially when direct access to complete environmental information is limited. To enable adaptability and facilitate integration with model predictive control, we propose a context-aware dynamics model based on neural ordinary differential equations, which infers environmental factors from state-action histories using a two-phase training procedure. We validate the approach across diverse robotic platforms, including a quadrotor in simulation, as well as a Sphero BOLT robot and a Fanuc manipulator in real-world experiments. The results demonstrate that our method effectively adapts to temporally and spatially varying environmental changes across different tasks. Videos are available at https://youtu.be/PY0sNyF2rqE , and the source code is available at https://github.com/syyu410-yu/context-aware-neural-ode-control.git .

Shao-Yi Yu, Jen-Wei Wang, Maya Horii +2

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

Robot Dynamics Identification Robot Dynamics Learning Robotics Neural ODEs Adaptive Control

Jun 8, 2026cs.RO

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

Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of payload variation, unmodeled coupling, friction, aerodynamic effects, and changing operating conditions. Most learning-based correction methods improve prediction accuracy by introducing a single additive residual, but do not preserve the internal mechanical structure of Euler--Lagrange systems. This leads to models that do not preserve symmetry, positive-definiteness, or the coupling between inertia and velocity-dependent terms, which can result in physically inconsistent predictions and reduced reliability when embedded in model-based controllers. We propose a structure-preserving residual learning framework that decomposes model mismatch into an inertia correction, the corresponding induced Coriolis term, and a generalized-force residual. The mechanical component is learned under physical constraints, while the disturbance-sensitive component is represented through a sparse history-dependent latent interaction model and adapted online using Bayesian linear regression. This separation preserves key mechanical structure while restricting adaptation to the part of the dynamics most affected by changing conditions. Experiments across multiple robotic platforms, including mobile, aerial, and manipulator systems, show that the proposed method improves dynamics prediction and trajectory tracking under coupled and time-varying dynamics. These results highlight the value of combining structured residual modeling, compact latent interaction selection, and selective online adaptation for real-world model-based control.

Rishabh Dev Yadav, Samaksh Ujjawal, Sihao Sun +2

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

Robotic Control Robot Dynamics Identification Online System Identification Residual Learning Residual Dynamics Learning Robot Dynamics Learning+2

Jun 2, 2026cs.RO

Hybrid Dynamics Modeling for a Flexible 2-DoF Robotic Arm

This paper examines three approaches for modeling the dynamics of a flexible-link 2-DoF robotic arm to address unmodeled dynamics not captured by rigid-body models. Two physics informed models combine rigid-body dynamics (RBD) formulations with a Gaussian Mixture Model (GMM) to capture residual model errors and linkage flexibility. A kinematics-based regression model serves as a purely data-driven baseline. Using an open-source dataset, torque predictions are first estimated using Ridge regression on kinematic features, while the physicsbased baseline is constructed from published specifications, and ordinary least-squares regression is subsequently used to estimate the same parameter set directly from data. Results show that the physics-based parameters yield the poorest accuracy, while regularized and least-squares estimators align more closely with measured torques. Residual analysis and error metrics highlight the limitations of purely parametric models for flexible-link systems and underscore the value of regularization and data-driven identification, supporting developments of semi-parametric residual learning methods.

Maciek Popik, Daniel Yang, Mahdis Bisheban

Dept. of Mechanical and Manufacturing Eng at the Schulich School of Engineering, University of Calgary, Alberta, Canada · Intelligent Dynamics and Control Lab, University of Calgary, Alberta, Canada

Robot Dynamics Identification Nonlinear System Identification Robotics Inverse Dynamics

May 23, 2026cs.RO

Geometric Workspace Analysis and Transmission-Aware Dynamics of a Serial Spherical Tool for Microsurgery

We present a kinematic and transmission-aware design framework for a serial spherical mechanism with an additional translational degree of freedom for microsurgery. The first contribution is an analytical workspace formulation that provides geometric insight into reachable motion and enables rapid selection of rotation axis orientations without numerical optimization. The second contribution is a dynamics-informed methodology for mechanisms driven by self-locking transmissions, supporting evaluation of torque requirements for a prescribed workspace geometry. The framework is accompanied by an open-source software package for friction identification and inverse dynamics analysis. Experiments on a purpose-built robotic tool for vitreoretinal surgery validate the predictive capability of the models and demonstrate their practical utility for engineering design.

Anestis Mablekos-Alexiou, Lyndon da Cruz, Christos Bergeles

Moorfields Eye Hospital NHS Foundation Trust, London, UK · King’s College London

Robot Dynamics Identification Robotics Surgical Robotics

May 18, 2026cs.RO

Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot

Developing dynamic models for tendon-driven continuum robots is challenging due to their nonlinear, high-dimensional, and friction-dominated dynamics. This paper presents a comparative study of data-driven system identification methods, including N4SID, ARX, and SINDYc, for modeling a tendon-actuated continuum robot with rolling joints developed at CERN. Despite the high number of joints of the robot, experimental analysis reveals that a two-degree-of-freedom dynamic model can accurately capture the system dynamics, owing to strong kinematic dependencies between the joints. The models are validated against experimental data, and used in the design of a model predictive controller, demonstrating their feasibility for real-time control.

Harald Minde Hansen, Bjørn Kåre Sæbø, Kristin Y. Pettersen +2

European Organization for Nuclear Research (CERN), Switzerland · Department of Engineering Cybernetics, Norwegian University of Science and Technology, NTNU, NO-7491 Trondheim, Norway

Robot Dynamics Identification Nonlinear System Identification System Identification Continuum Robot Control Tendon-Driven Actuation Soft Robotics

May 17, 2026cs.RO

PRIME: Physically-consistent Robotic Inertial and Motion Estimation for Legged and Humanoid Robots

Humanoid and legged robots interact with the environment through intermittent contacts, making accurate motion estimation fundamentally dependent on reasoning about contact dynamics. However, standard sensing pipelines-whether based on onboard proprioception with Extended Kalman Filters (EKFs) or external motion capture systems-recover only kinematics, while contact forces, contact timing, and inertial parameters remain unobserved. As a result, purely kinematic reconstructions often violate rigid-body dynamics, particularly during contact-rich motions. To enable accurate motion estimation from onboard kinematics in real-world deployment, we propose PRIME (Physically-consistent Robotic Inertial and Motion Estimation), a Maximum A Posteriori (MAP) formulation that refines measured kinematics and actuator commands into a dynamically consistent trajectory while jointly estimating frictional contact forces and physically consistent inertial parameters. Our approach incorporates differentiable contact dynamics with smoothed complementarity constraints and an Anitescu-style friction model, yielding a smooth optimization problem that remains tractable across versatile contact transitions. We evaluate PRIME on contact-rich locomotion with quadrupedal robots and the Unitree G1 humanoid, demonstrating improved trajectory consistency and accurate inertial parameter identification. Beyond improving state estimation and feedback control with calibrated inertial parameters, PRIME produces force- and contact-annotated motion reconstructions from real robots in deployment, which can be used to provide high-quality data for downstream learning applications, including large-scale behavior modeling and robot foundation models.

Jiarong Kang, Kunzhao Ren, Tao Pang +1

University of Wisconsin-Madison · Shanghai Innovation Institute

Robot Dynamics Identification Robot Locomotion System Identification Robot State Estimation

May 15, 2026cs.RO

A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm

This paper presents a reproducible and physically feasible dynamic parameter identification framework for CRANE-X7, a low-cost robot arm driven by modular smart actuators. To improve practical identifiability, products of inertia are removed according to approximate link symmetry, reducing the rigid-body model from 65 to 39 base parameters. Identification motions are hand-designed from structured single-joint and adjacent-joint primitives under practical joint-range limits. The proposed pipeline combines preprocessing, inverse-dynamics-regressor-based ordinary least squares (OLS), conditional semidefinite-programming (SDP) projection for feasibility recovery, and closed-loop input error (CLIE) refinement. Candidate solutions from 40 structured trajectories are analyzed in a common principal component analysis (PCA) space to select a statistically central representative model. Because statistical centrality alone does not ensure physical acceptability, the selected model is finally screened by an all-pose positive-definiteness audit of the inertia matrix and, when necessary, corrected by a localized post-CLIE SDP rescue step. Experiments show that the parameter cloud becomes progressively more concentrated from OLS to SDP and CLIE, while the final accepted model preserves high predictive accuracy on held-out validation motions. These results demonstrate a practical route to statistically coherent and physically feasible dynamic models for low-cost robot platforms.

Junji Oaki, Koki Yamane, Koki Inami +1

Institute of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

Robot Dynamics Identification Parameter Identifiability Nonlinear System Identification System Identification

Apr 23, 2026cs.RO

Wiggle and Go! System Identification for Zero-Shot Dynamic Rope Manipulation

Many robotic tasks are unforgiving; a single mistake in a dynamic throw can lead to unacceptable delays or unrecoverable failure. We introduce Wiggle and Go!, a two-stage framework for zero-shot rope manipulation: a brief, safe wiggle action is observed to predict descriptive rope parameters, which then conditions a trajectory optimizer for zero-shot goal-conditioned execution. Unlike prior dynamic rope manipulation methods that require large real-world datasets or iterative real-world refinement, our identification module is task-agnostic, supporting diverse manipulation policies without retraining. We achieve a 3.55,cm average accuracy on 3D target striking in real using rope system parameters in comparison to 15.29,cm for uninformed baselines, and over 50% success on multi-objective lobbing and draping tasks. Predicted parameters transfer to unseen motions with 0.95 Pearson correlation between simulated and real rope dynamics, indicating that the identification module generalizes across the task corpus. Project website: https://wiggleandgo.github.io/

Arthur Jakobsson, Abhinav Mahajan, Karthik Pullalarevu +6

Robotics Institute, Carnegie Mellon University · University of North Carolina at Chapel Hill

Robot Dynamics Identification Deformable Object Manipulation Robotic Manipulation System Identification

Mar 15, 2026cs.RO

Coordinate-Independent Robot Model Identification

Robot model identification is commonly performed by least-squares regression on inverse dynamics, but existing formulations measure residuals directly in coordinate force space and therefore depend on the chosen coordinate chart, units, and scaling. This paper proposes a coordinate-independent identification method that weights inverse-dynamics residuals by the dual metric induced by the system Riemannian metric. Using the force--velocity vector--covector duality, the dual metric provides a physically meaningful normalization of generalized forces, pulling coordinate residuals back into the ambient mechanical space and eliminating coordinate-induced bias. The resulting objective remains convex through an affine-metric and Schur-complement reformulation, and is compatible with physical-consistency constraints and geometric regularization. Experiments on an inertia-dominated Crazyflie--pendulum system and a drag-dominated LandSalp robot show improved identification accuracy, especially on shape coordinates, on both downsampled and full datasets.

Yanhao Yang, Ross L. Hatton

Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University, Corvallis, OR USA

Robot Dynamics Identification System Identification Inverse Dynamics

Feb 2, 2026cs.RO

Bridging the Sim-to-Real Gap with multipanda_ros2: A Real-Time ROS2 Framework for Multimanual Systems

We present

multipanda\_ros2

, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 control, this framework provides native ROS2 interfaces for controlling any number of robots from a single process. Our core contributions address key challenges in real-time torque control, including interaction control and robot-environment modeling. A central focus of this work is sustaining a 1kHz control frequency, a necessity for real-time control and a minimum frequency required by safety standards. Moreover, we introduce a controllet-feature design pattern that enables controller-switching delays of

\le 2

ms, facilitating reproducible benchmarking and complex multi-robot interaction scenarios. To bridge the simulation-to-reality (sim2real) gap, we integrate a high-fidelity MuJoCo simulation with quantitative metrics for both kinematic accuracy and dynamic consistency (torques, forces, and control errors). Furthermore, we demonstrate that real-world inertial parameter identification can significantly improve force and torque accuracy, providing a methodology for iterative physics refinement. Our work extends approaches from soft robotics to rigid dual-arm, contact-rich tasks, showcasing a promising method to reduce the sim2real gap and providing a robust, reproducible platform for advanced robotics research.

Jon Škerlj, Seongjin Bien, Abdeldjallil Naceri +1

Munich Institute of Robotics and Machine Intelligence (MIRMI), Technische Universität München (TUM), Germany · Technische Universität Nürnberg (UTN), Germany · Mohamed Bin Zayed University of Artificial Intelligence, UAE

Robotic Control Robot Dynamics Identification Multi-Robot Systems Robotics Simulation Sim-to-Real Transfer Robotics