7 Conclusions
This survey has reviewed the growing body of work on the robotic perception and manipulation of Deformable Linear Objects (DLOs). Designed as both an introduction for newcomers and a roadmap for experienced researchers, the literature reveals a clear takeaway: while DLO manipulation is a highly active and advancing area of research, it has not yet reached the maturity required for widespread industrial use.
To understand this maturity gap compared to established domains like rigid manipulation or navigation, we must reevaluate what constitutes a ``baseline''. In mature fields, capabilities like tactile sensing and robustness to unstructured environments are often treated as advanced upgrades to an already functional system. For DLO manipulation, however, these capabilities are not optional enhancements; they are fundamental prerequisites for achieving even basic reliability.
Consider, for example, the coupling between tactile sensing and deformation. Simply touching a DLO to measure its state can inadvertently actuate it. This physical contact perturbs the object, significantly increasing measurement uncertainty. This problem peaks in near-singular configurations (such as buckling, pigtailing, or transitioning from slack to taut regimes). In these states, visual feedback cannot detect accumulated mechanical stress, making tactile data crucial; yet, it is exactly in these regimes that the DLO is most sensitive to touch.
Moreover, DLOs are inherently unstructured. Even in controlled environments, a DLO can generate complex and cluttered conditions on its own, including self-occlusions, knots, tangles, and high-friction self-contacts. While other robotics domains usually attribute unstructured conditions to external factors (like poor lighting or environmental obstacles in navigation), DLOs generate extreme complexity purely through their own geometry and deformation. This, robustness against these internal dynamics is mandatory.
The main challenge to establishing standard, replicable baselines is this intrinsic physical complexity. DLOs are highly nonlinear, underactuated systems plagued by singularities and abrupt behavioral shifts. Their configuration space explodes when accounting for varying shapes, materials, and grasp points. Because of this, purely data-driven approaches are rarely sufficient. Typically, black-box learning models (like foundation models) lack the interpretability and provable guarantees needed for industrial safety. Moving forward, the most promising path is to fuse the adaptability and context-awareness of modern machine learning with the rigorous stability and safety guarantees of classical mechanics and systems analysis.