Force-Based Semantic Representation and Estimation of Feature Points for Robotic Cable Manipulation with Environmental Contacts

This work demonstrates the utility of dual-arm robots with dual-wrist force-torque sensors in manipulating a Deformable Linear Object (DLO) within an unknown environ- ment that imposes constraints on the DLO’s movement through contacts and fixtures. We propose a strategy to estimate the pose of unknown environmental contacts encountered during the manipulation of a DLO, classifying the induced constraints as unilateral, bilateral and fully constrained, exploiting the redundancy of force sensors. A semantic approach to define environmental constraints is introduced and incorporated into a graph-based model of the DLO. This model remains accurate as long as the DLO is under tension and is dynamically updated throughout the manipulation process, built by sequencing a set of primitives. The estimation strategy is validated through simulations and real-world experiments, demonstrating its po- tential in handling DLOs under various, possibly uncertain, constraints.