Shape Control of a Planar Hyper-Redundant Robot via Hybrid Kinematics-Informed and Learning-based Approach

2026-03-11

AI Summary

This work addresses the challenge of unstable shape control in flexible hyper-redundant robots, whose structural compliance renders them highly susceptible to disturbances. To overcome this, the authors propose SpatioCoupledNet—a hybrid control framework that integrates kinematic priors with data-driven learning. The method employs a hierarchical neural network to explicitly model bidirectional spatial coupling between segments and local perturbations, complemented by a state-dependent confidence gating mechanism that adaptively fuses model-based predictions with learned components. Experimental validation on a five-segment planar robot demonstrates significant improvements: steady-state error is reduced by up to 75.5%, convergence speed increases by 20.5%, and the average end-effector positioning error during dynamic obstacle avoidance tasks reaches 10.47 mm.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Hyper-redundant robots offer high dexterity, making them good at operating in confined and unstructured environments. To extend the reachable workspace, we built a multi-segment flexible rack actuated planar robot. However, the compliance of the flexible mechanism introduces instability, rendering it sensitive to external and internal uncertainties. To address these limitations, we propose a hybrid kinematics-informed and learning-based shape control method, named SpatioCoupledNet. The neural network adopts a hierarchical design that explicitly captures bidirectional spatial coupling between segments while modeling local disturbance along the robot body. A confidence-gating mechanism integrates prior kinematic knowledge, allowing the controller to adaptively balance model-based and learned components for improved convergence and fidelity. The framework is validated on a five-segment planar hyper-redundant robot under three representative shape configurations. Experimental results demonstrate that the proposed method consistently outperforms both analytical and purely neural controllers. In complex scenarios, it reduces steady-state error by up to 75.5% against the analytical model, and accelerates convergence by up to 20.5% compared to the data-driven baseline. Furthermore, gating analysis reveals a state-dependent authority fusion, shifting toward data-driven predictions in unstable states, while relying on physical priors in the remaining cases. Finally, we demonstrate robust performance in a dynamic task where the robot maintains a fixed end-effector position while avoiding moving obstacles, achieving a precise tip-positioning accuracy with a mean error of 10.47 mm.

Problem

• hyper-redundant robot
• shape control
• flexible mechanism
• uncertainty
• instability

Innovation

• hyper-redundant robot
• hybrid control
• spatial coupling
• confidence-gating mechanism
• kinematics-informed learning