Estimating Force Interactions of Deformable Linear Objects from their Shapes

• URL: http://arxiv.org/abs/2602.01085v1
• Date: Sun, 01 Feb 2026 08:02:30 GMT
• Title: Estimating Force Interactions of Deformable Linear Objects from their Shapes
• Abstract summary: In many robot-wire interaction tasks, contact occurs not at the end-effector but at other points along the robot's body.<n>Existing approaches often rely on expensive external force-torque sensor or that contacts occur at the end-effector for accurate force estimation.<n>This is achieved by exploiting derived consistency conditions and solving a system of linear equations based on force-torque balance along the wire.
• Score: 10.735269120154983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces an analytical approach for detecting and estimating external forces acting on deformable linear objects (DLOs) using only their observed shapes. In many robot-wire interaction tasks, contact occurs not at the end-effector but at other points along the robot's body. Such scenarios arise when robots manipulate wires indirectly (e.g., by nudging) or when wires act as passive obstacles in the environment. Accurately identifying these interactions is crucial for safe and efficient trajectory planning, helping to prevent wire damage, avoid restricted robot motions, and mitigate potential hazards. Existing approaches often rely on expensive external force-torque sensor or that contacts occur at the end-effector for accurate force estimation. Using wire shape information acquired from a depth camera and under the assumption that the wire is in or near its static equilibrium, our method estimates both the location and magnitude of external forces without additional prior knowledge. This is achieved by exploiting derived consistency conditions and solving a system of linear equations based on force-torque balance along the wire. The approach was validated through simulation, where it achieved high accuracy, and through real-world experiments, where accurate estimation was demonstrated in selected interaction scenarios.

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