Lightweight Kinematic and Static Modeling of Cable-Driven Continuum Robots via Actuation-Space Energy Formulation

Continuum robots, inspired by octopus arms and elephant trunks, combine dexterity with intrinsic compliance, making them well suited for unstructured and confined environments. Yet their continuously deformable morphology poses challenges for motion planning and control, calling for accurate but lightweight models. We propose the Lightweight Actuation Space Energy Modeling (LASEM) framework for cable driven continuum robots, which formulates actuation potential energy directly in actuation space. LASEM yields an analytical forward model derived from geometrically nonlinear beam and rod theories via Hamilton's principle, while avoiding explicit modeling of cable backbone contact. It accepts both force and displacement inputs, thereby unifying kinematic and static formulations. Assuming the friction is neglected, the framework generalizes to nonuniform geometries, arbitrary cable routings, distributed loading and axial extensibility, while remaining computationally efficient for real-time use. Numerical simulations validate its accuracy, and a semi-analytical iterative scheme is developed for inverse kinematics. To address discretization in practical robots, LASEM further reformulates the functional minimization as a numerical optimization, which also naturally incorporates cable potential energy without explicit contact modeling.

Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images

Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance Segmentation in dense images. PerSense employs a novel Instance Detection Module (IDM) that leverages density maps (DMs) to generate instance-level candidate point prompts, followed by a Point Prompt Selection Module (PPSM) that filters false positives via adaptive thresholding and spatial gating. A feedback mechanism further enhances segmentation by automatically selecting effective exemplars to improve DM quality. We additionally present PerSense++, an enhanced variant that incorporates three additional components to improve robustness in cluttered scenes: (i) a diversity-aware exemplar selection strategy that leverages feature and scale diversity for better DM generation; (ii) a hybrid IDM combining contour and peak-based prompt generation for improved instance separation within complex density patterns; and (iii) an Irrelevant Mask Rejection Module (IMRM) that discards spatially inconsistent masks using outlier analysis. Finally, to support this underexplored task, we introduce PerSense-D, a dedicated benchmark for personalized segmentation in dense images. Extensive experiments across multiple benchmarks demonstrate that PerSense++ outperforms existing methods in dense settings.