Model-Less Feedback Control of Space-based Continuum Manipulators using Backbone Tension Optimization

• URL: http://arxiv.org/abs/2512.06754v1
• Date: Sun, 07 Dec 2025 09:34:28 GMT
• Title: Model-Less Feedback Control of Space-based Continuum Manipulators using Backbone Tension Optimization
• Abstract summary: Continuum manipulators offer dexterity and safe compliance for navigation within confined and obstacle-rich environments.<n>Their infinite-dimensional backbone deformation, unmodeled internal friction, and configuration-dependent stiffness limit the reliability of model-based kinematic formulations.<n>This work presents a complete model-less control framework that bypasses kinematic modeling by using an empirically intrinsic Jacobian refined online through differential convex updates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continuum manipulators offer intrinsic dexterity and safe geometric compliance for navigation within confined and obstacle-rich environments. However, their infinite-dimensional backbone deformation, unmodeled internal friction, and configuration-dependent stiffness fundamentally limit the reliability of model-based kinematic formulations, resulting in inaccurate Jacobian predictions, artificial singularities, and unstable actuation behavior. Motivated by these limitations, this work presents a complete model-less control framework that bypasses kinematic modeling by using an empirically initialized Jacobian refined online through differential convex updates. Tip motion is generated via a real-time quadratic program that computes actuator increments while enforcing tendon slack avoidance and geometric limits. A backbone tension optimization term is introduced in this paper to regulate axial loading and suppress co-activation compression. The framework is validated across circular, pentagonal, and square trajectories, demonstrating smooth convergence, stable tension evolution, and sub-millimeter steady-state accuracy without any model calibration or parameter identification. These results establish the proposed controller as a scalable alternative to model-dependent continuum manipulation in a constrained environment.

Related papers

• Nonlinear Model Predictive Control via Sequential Convex Programming for Drone-to-Drone Docking [0.0]
  This work formulates the docking task as a finite-horizon optimal control problem based on a reduced-order nonlinear model augmented with disturbance states.<n>The resulting problem is solved using sequential convex programming within a receding-horizon framework to generate dynamically feasible docking trajectories.
  arXiv  Detail & Related papers  (2026-08-11T06:27:45Z)
• A Task-Space Receding Horizon Controller for Fast Collision Avoidance [0.08594140167290099]
  Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion.<n>Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints.<n>We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference.
  arXiv  Detail & Related papers  (2026-07-17T08:13:12Z)
• Morphology-Specific Closed-Loop Control of Logarithmic-Spiral Continuum Arms via Online Jacobian Error Compensation [9.759803937074699]
  Recently reported logarithmic-spiral robots demonstrated scalable fabrication and versatile grasping but lacked inverse kinematics and closed-loop control.<n>This work presents the first morphology-specific closed-loop task-space control framework for logarithmic-spiral continuum arms.<n>Results demonstrate that combining logarithmic-spiral morphology with online Jacobian compensation enables accurate, robust, and scalable control of highly underactuated continuum manipulators.
  arXiv  Detail & Related papers  (2026-06-24T13:54:23Z)
• Taming Curvature: Architecture Warm-Up for Stable Transformer Training [52.6838951211596]
  We introduce a fast online estimator of the largest (preconditioned) Hessian eigenvalue (i.e., curvature) based on a warm-started variant for power with Hessian-vector products.<n>We find that training instabilities coincide with surges in preconditioned curvature and that curvature grows with depth.<n> Experiments on large Transformers validate that our approach enables efficient curvature tracking and reduces instabilities.
  arXiv  Detail & Related papers  (2026-06-15T14:16:56Z)
• Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics [3.247008128557575]
  Most learning-based correction methods improve prediction accuracy by introducing a single additive residual.<n>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.<n> 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.
  arXiv  Detail & Related papers  (2026-06-08T15:36:00Z)
• Adaptive Smooth Tchebycheff Attention for Multi-Objective Policy Optimization [55.338784037660474]
  Multi- curvature reinforcement learning in domains requires balancing complex, non-fragile interference trade-offs between conflicting objectives.<n>In this paper we introduce a novel based real-time based real-time optimization on a robotic visual -- a proxy for monitoring ecosystems.
  arXiv  Detail & Related papers  (2026-05-12T21:32:57Z)
• Rollback-Free Stable Brick Structures Generation [51.856925926010184]
  Existing approaches rely on external physical simulators during inference to perform rejection sampling and brick-by-brick rollbacks.<n>We propose a reinforcement learning paradigm that shifts physical validity enforcement from test-time correction to training-time policy optimization.<n> Experimental results demonstrate that our approach achieves state-of-the-art generation quality while accelerating inference speed by orders of magnitude.
  arXiv  Detail & Related papers  (2026-05-07T21:06:44Z)
• Real-Time Projected Adaptive Control for Closed-Chain Co-Manipulative Continuum Robots [0.764671395172401]
  In co-manipulative continuum robots ( CCRs), multiple continuum arms cooperate by grasping a common flexible object.<n>In practical tasks, the flexible object's physical parameters are often unknown and vary between operations.<n>This paper presents a projected adaptive control framework for CCRs formulated at the dynamic level.
  arXiv  Detail & Related papers  (2026-04-05T22:03:23Z)
• Statistical Contraction for Chance-Constrained Trajectory Optimization of Non-Gaussian Stochastic Systems [0.0]
  Our framework employs conformal inference to generate confidence sets for the closed-loop dynamics around arbitrary reference trajectories.<n>Via appropriate constraint tightening, chance constraints can be reformulated into tractable, statistically valid deterministic constraints on the reference trajectories.<n>This enables a formal pathway to leverage and validate learning-based motion planners and controllers, such as those with neural contraction metrics, in safety-critical real-world applications.
  arXiv  Detail & Related papers  (2026-03-07T08:01:20Z)
• Constraint-Free Static Modeling of Continuum Parallel Robot [2.1933060555769073]
  Continuum parallel robots (CPR) combine rigid actuation mechanisms with multiple elastic rods in a closed-loop topology.<n>This paper presents a geometric exact, configuration-based and constraint-free static model of CPR that remains valid under geometrically nonlinear, large-deformation and large-rotation conditions.
  arXiv  Detail & Related papers  (2026-03-05T15:49:30Z)
• KoopGen: Koopman Generator Networks for Representing and Predicting Dynamical Systems with Continuous Spectra [65.11254608352982]
  We introduce a generator-based neural Koopman framework that models dynamics through a structured, state-dependent representation of Koopman generators.<n>By exploiting the intrinsic Cartesian decomposition into skew-adjoint and self-adjoint components, KoopGen separates conservative transport from irreversible dissipation.
  arXiv  Detail & Related papers  (2026-02-15T06:32:23Z)
• TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generation [45.864084191741135]
  Post-training is the decisive step for converting a pretrained video generator into a production-oriented model.<n>This report presents a systematical post-training framework that organizes supervised policy shaping, reward-driven reinforcement learning, and preference-based refinement.
  arXiv  Detail & Related papers  (2026-02-07T15:49:25Z)
• Dual-quaternion learning control for autonomous vehicle trajectory tracking with safety guarantees [39.146761527401424]
  We propose a learning-based trajectory tracking controller for autonomous robotic platforms whose motion can be described kinematically.<n>The controller is formulated in the dual quaternion framework and operates at the velocity level, assuming direct command of angular and linear velocities.
  arXiv  Detail & Related papers  (2026-01-06T15:30:02Z)
• 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)
• AERMANI-Diffusion: Regime-Conditioned Diffusion for Dynamics Learning in Aerial Manipulators [2.376629619475859]
  Aerial manipulators undergo rapid, configuration-dependent changes in inertial coupling forces and aerodynamic forces.<n>We propose a regime-conditioned diffusion framework that models the full distribution of residual forces using a conditional diffusion process and a lightweight temporal encoder.
  arXiv  Detail & Related papers  (2025-12-11T16:10:32Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control [1.9116784879310027]
  Deep learning is increasingly used for enforce physical systems or systems where first-principles modeling is difficult.<n>We develop a new type of deep learning model that satisfies constraints on Jacobian entries--that monotonicity, and sign-definite positivity.<n>On a two-tank system hybrid, the proposed approach improves control inputs than existing methods.
  arXiv  Detail & Related papers  (2025-09-24T08:15:26Z)
• Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding [55.107555305760954]
  We propose a conceptually simple yet effective method that attributes forgetting to layer-wise parameter overwriting and the resulting decision boundary distortion. Our method achieves competitive accuracy performance, even with absolute superiority of zero exemplar buffer and 1.02x the base model.
  arXiv  Detail & Related papers  (2024-01-17T09:01:29Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)

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.