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

• URL: http://arxiv.org/abs/2605.15949v1
• Date: Fri, 15 May 2026 13:36:03 GMT
• Title: A Reproducible and Physically Feasible Dynamic Parameter Identification Framework for a Low-Cost Robot Arm
• Abstract summary: This paper presents a dynamic parameter identification framework for a low-cost robot arm driven by smart actuators.<n>Products of inertia are removed according to approximate link symmetry, reducing the rigid-body model from 65 to 39 base parameters.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 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.

Related papers

• Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats [76.98547580312116]
  This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs)<n>To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent avoidance.
  arXiv  Detail & Related papers  (2026-07-22T16:37:05Z)
• A Methodology for Dynamic Parameters Identification of 3-DOF Parallel Robots in Terms of Relevant Parameters [0.19999259391104393]
  The identification of dynamic parameters in mechanical systems is important for improving model-based control and for performing realistic simulations.<n>A strategy for dynamic parameter identification of fully parallel robots in terms of a subset called relevant parameters is put forward.<n>The application of the propose strategy has been experimentally tested on two difierent configurations of actual 3-DOF parallel robots.
  arXiv  Detail & Related papers  (2026-03-16T13:23:34Z)
• Shape Control of a Planar Hyper-Redundant Robot via Hybrid Kinematics-Informed and Learning-based Approach [42.55947704321053]
  Hyper-redundant robots offer high dexterity, making them good at operating in confined and unstructured environments.<n>To extend the reachable workspace, we built a multi-segment flexible rack actuated planar robot.<n>We propose a hybrid kinematics-informed and learning-based shape control method, named SpatioCoupledNet.
  arXiv  Detail & Related papers  (2026-03-11T04:35:06Z)
• Efficient Real-Time Adaptation of ROMs for Unsteady Flows Using Data Assimilation [7.958594167693376]
  We propose an efficient retraining strategy for a parameterized Reduced Order Model (ROM)<n>The strategy attains accuracy comparable to full retraining while requiring only a fraction of the computational time.<n>We show that, for the dynamical system considered, the dominant source of error in out-of-sample forecasts stems from distortions of the latent manifold.
  arXiv  Detail & Related papers  (2026-02-26T16:43:28Z)
• Adaptive Linear Path Model-Based Diffusion [52.84663832658799]
  We introduce Linear Path Model-Based Diffusion (LP-MBD), which replaces the variance-preserving schedule with a flow-matching linear probability path.<n>We also propose Adaptive LP-MBD (ALP-MBD), which leverages reinforcement learning to adjust diffusion steps and noise levels according to task complexity and environmental conditions.
  arXiv  Detail & Related papers  (2026-02-02T21:33:03Z)
• Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
  We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
  arXiv  Detail & Related papers  (2026-02-01T12:56:10Z)
• Fractional-order Modeling for Nonlinear Soft Actuators via Particle Swarm Optimization [49.34452620841728]
  This paper proposes an innovative modeling framework based on fractional-order differential equations (FODEs) to capture the dynamic behavior of soft materials.<n>The proposed approach effectively represents the complex deformation phenomena inherent in soft actuators.
  arXiv  Detail & Related papers  (2025-12-20T04:46:25Z)
• Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging [75.93960998357812]
  Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their capabilities across different tasks and domains.<n>Current model merging techniques focus on merging all available models simultaneously, with weight matrices-based methods being the predominant approaches.<n>We propose a training-free projection-based continual merging method that processes models sequentially.
  arXiv  Detail & Related papers  (2025-01-16T13:17:24Z)
• Formal Controller Synthesis for Markov Jump Linear Systems with Uncertain Dynamics [64.72260320446158]
  We propose a method for synthesising controllers for Markov jump linear systems. Our method is based on a finite-state abstraction that captures both the discrete (mode-jumping) and continuous (stochastic linear) behaviour of the MJLS. We apply our method to multiple realistic benchmark problems, in particular, a temperature control and an aerial vehicle delivery problem.
  arXiv  Detail & Related papers  (2022-12-01T17:36:30Z)
• Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions [0.0]
  This work presents a data-driven, non-intrusive framework which combines ROM construction with reduced dynamics identification. The proposed approach leverages autoencoder neural networks with parametric sparse identification of nonlinear dynamics (SINDy) to construct a low-dimensional dynamical model. These aim at tracking the evolution of periodic steady-state responses as functions of system parameters, avoiding the computation of the transient phase, and allowing to detect instabilities and bifurcations.
  arXiv  Detail & Related papers  (2022-11-13T01:57:18Z)
• Robots State Estimation and Observability Analysis Based on Statistical Motion Models [10.941793802354953]
  This paper presents a generic motion model to capture mobile robots' dynamic behaviors (translation and rotation) The model is based on statistical models driven by white random processes and is formulated into a full state estimation algorithm based on the error-state extended Kalman filtering framework (ESEKF) A novel texttextbfitthin set concept is introduced to characterize the unobservable subset of the system states.
  arXiv  Detail & Related papers  (2020-10-12T18:35:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.