Real-Time Projected Adaptive Control for Closed-Chain Co-Manipulative Continuum Robots

• URL: http://arxiv.org/abs/2604.04286v1
• Date: Sun, 05 Apr 2026 22:03:23 GMT
• Title: Real-Time Projected Adaptive Control for Closed-Chain Co-Manipulative Continuum Robots
• Abstract summary: 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.
• Score: 0.764671395172401
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In co-manipulative continuum robots (CCRs), multiple continuum arms cooperate by grasping a common flexible object, forming a closed-chain deformable mechanical system. The closed-chain coupling induces strong dynamic interactions and internal reaction forces. Moreover, in practical tasks, the flexible object's physical parameters are often unknown and vary between operations, rendering nominal model-based controllers inadequate. This paper presents a projected adaptive control framework for CCRs formulated at the dynamic level. The coupled dynamics are expressed using the Geometric Variable Strain (GVS) representation, yielding a finite-dimensional model that accurately represents the system, preserves the linear-in-parameters structure required for adaptive control, and is suitable for real-time implementation. Closed-chain interactions are enforced through Pfaffian velocity constraints, and an orthogonal projection is used to express the dynamics in the constraint-consistent motion subspace. Based on the projected dynamics, an adaptive control law is developed to compensate online for uncertain dynamic parameters of both the continuum robots and the manipulated flexible object. Lyapunov analysis establishes closed-loop stability and convergence of the task-space tracking errors to zero. Simulation and experiments on a tendon-driven CCR platform validate the proposed framework in task-space regulation and trajectory tracking.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• 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)
• Learning Context-Aware Neural ODE Dynamics for Adaptive Robotic Control [55.01272784312405]
  We propose a context-aware dynamics model based on neural ordinary differential equations.<n>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.
  arXiv  Detail & Related papers  (2026-06-13T20:55:46Z)
• Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation [3.247008128557575]
  We propose a structured encoder-decoder framework for adaptive residual dynamics learning in aerial manipulators.<n>Results show improved residual prediction accuracy, faster adaptation under changing operating conditions, and enhanced MPC-based trajectory tracking performance.
  arXiv  Detail & Related papers  (2026-05-14T13:17:04Z)
• Trajectory Generation for Underactuated Soft Robot Manipulators using Discrete Elastic Rod Dynamics [2.739506394240299]
  Planning motions with dynamically-feasible trajectories requires models that capture arbitrary deformations.<n>We present our trajectory generation framework and validate it experimentally on a pneumatic soft robotic limb.
  arXiv  Detail & Related papers  (2026-03-23T22:01:14Z)
• ACLM: ADMM-Based Distributed Model Predictive Control for Collaborative Loco-Manipulation [9.708461585583791]
  Collaborative transportation of heavy payloads via loco-manipulation is a challenging yet essential capability for legged robots.<n>This study proposes an Alternating Direction Method of Multipliers-based distributed model predictive control framework for loco-manipulation.
  arXiv  Detail & Related papers  (2026-03-07T08:06:51Z)
• Curve-Induced Dynamical Systems on Riemannian Manifolds and Lie Groups [25.48711199625679]
  We introduce Curve-induced Dynamical systems on Smooth Manifolds (CDSM), a real-time framework for constructing dynamical systems directly on Lie groups.<n>We demonstrate improved trajectory accuracy, reduced path deviation, and faster generation and query times compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-03-05T15:18:26Z)
• Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy [52.106797722292896]
  We present DCDP, a Dynamic Closed-Loop Diffusion Policy framework that integrates chunk-based action generation with real-time correction.<n>In dynamic PushT simulations, DCDP improves adaptability by 19% without retraining while requiring only 5% additional computation.
  arXiv  Detail & Related papers  (2026-03-02T15:04:18Z)
• Constraining Streaming Flow Models for Adapting Learned Robot Trajectory Distributions [10.615762867055677]
  Streaming Flow Policies (SFPs) have emerged as a powerful paradigm for generating robot trajectories by integrating learned velocity fields directly in action space.<n>We propose Constraint-Aware Streaming Flow (CASF), a framework that augments streaming flow policies with constraint-dependent metrics that reshape the learned velocity field during execution.<n>We demonstrate CASF in simulated and real-world manipulation tasks, showing that it produces constraint-satisfying trajectories that remain smooth, feasible, and dynamically consistent.
  arXiv  Detail & Related papers  (2026-02-17T13:27:05Z)
• Model-Less Feedback Control of Space-based Continuum Manipulators using Backbone Tension Optimization [0.0]
  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.
  arXiv  Detail & Related papers  (2025-12-07T09:34:28Z)
• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control [46.81433026280051]
  We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
  arXiv  Detail & Related papers  (2022-10-23T00:45:05Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• Consolidating Kinematic Models to Promote Coordinated Mobile Manipulations [96.03270112422514]
  We construct a Virtual Kinematic Chain (VKC) that consolidates the kinematics of the mobile base, the arm, and the object to be manipulated in mobile manipulations. A mobile manipulation task is represented by altering the state of the constructed VKC, which can be converted to a motion planning problem.
  arXiv  Detail & Related papers  (2021-08-03T02:59:41Z)

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.