VSL-Skin: Individually Addressable Phase-Change Voxel Skin for Variable-Stiffness and Virtual Joints Bridging Soft and Rigid Robots
Authors: Zihan Oliver Zeng, Jiajun An, Preston Luk, Upinder Kaur
Organizations: School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA. · Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA. · School of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, USA.
Soft robots are compliant but often cannot support loads or hold their shape, while rigid robots provide structural strength but are less adaptable. Existing variable-stiffness systems usually operate at the scale of whole segments or patches, which limits precise control over stiffness distribution and virtual joint placement. This paper presents the Variable Stiffness Lattice Skin (VSL-Skin), the first system to enable individually addressable voxel-level morphological control with centimeter-scale precision. The system provides three main capabilities: nearly two orders of magnitude stiffness modulation across axial (15-1200 N/mm), shear (45-850 N/mm), bending (810^2 - 310^4 N/deg), and torsional modes with centimeter-scale spatial control; the first demonstrated 30% axial compression in phase-change systems while maintaining structural integrity; and autonomous component-level self-repair through thermal cycling, which eliminates fatigue accumulation and enables programmable sacrificial joints for predictable failure management. Selective voxel activation creates six canonical virtual joint types with programmable compliance while preserving structural integrity in non-activated regions. The platform incorporates closed-form design models and finite element analysis for predictive synthesis of stiffness patterns and joint placement. Experimental validation demonstrates 30% axial contraction, thermal switching in 75-second cycles, and cut-to-fit integration that preserves addressability after trimming. The row-column architecture enables platform-agnostic deployment across diverse robotic systems without specialized infrastructure. This framework establishes morphological intelligence as an engineerable system property and advances autonomous reconfigurable robotics.
Figures & tables
Fig. 1 : VSL-SKin allows dynamically-controllable morphologies through a modular design.
Fig. 2 : Normalized stiffness comparison of candidate lattices under fixed-mass constraints.
Fig. 3 : (a) Field’s metal insert molding. (b) Carbon black heating element molding. (c) Assemble using the guide. (d) Encapsulate and seal components using Dragon Skin.
Fig. 4 : Parametric studies of voxel-level design. (Left) Variation of thickness; (Right) variation of angular resolution.
Fig. 5 : Chart (log scale) showing the programmable stiffness range across axial, shear, bending, and torsional modes.
Fig. 6 : Six programmable joint configurations (a–f). Each panel shows left : activation matrix (brown = activated, gray = deactivated), middle : simulated elastic strain field, and right : experimental deformed shape. Strain localizes to the activated voxels, enabling placement-specific compliance.
Fig. 7 : Programmable axial shortening and thermal Field. (a) Experimental contraction of the lattice sleeve under activation of longitudinal bands, achieving 30% axial shortening. (b) Thermo-electric simulation of a voxel during resistive heating showing mid-span warming ahead of the vertices.
Form
System
Mod.
Control
DL
LC
VJ
Phase-ch.
Al-Harthy et al. [ 35 ]
∼43×
Thermal
✗
✗
✓
Lattice,
VSL-Skin*
∼80× a
Joule (PWM)
✓
✓
✓
phase-ch.
Layer-jam.
Shah et al. [ 36 ]
∼47×
Vacuum
✗
✗
✓
Gran.-jam.
Hauser et al. [ 37 ]
>7×
Vacuum
✗
✗
✗
Fiber-jam.
Yang et al. [ 38 ]
>20× b
Vacuum
✗
✗
✓
TABLE I : Stiffness-modulation Benchmarking
Fig. 8 : Damage–rework–integration of the VSL skin: (a) CAD; (b) deliberate four-voxel fracture on the fabricated skin; (c) trimmed aperture; (d) longitudinal adhesive seam; (e) installation on a robot arm with a through-axis sensor.
This paper presents a novel, modular, cable-driven soft robotic arm featuring multi-segment reconfigurability. The proposed architecture enables a stackable system with independent segment control, allowing scalable adaptation to diverse structural and application requirements. The system is fabricated from soft silicone material and incorporates embedded tendon-routing channels with a protective dual-helical tendon structure. Experimental results showed that modular stacking substantially expanded the reachable workspace: relative to the single-segment arm, the three-segment configuration achieved up to a 13-fold increase in planar workspace area and a 38.9-fold increase in workspace volume. Furthermore, this study investigated the effect of silicone stiffness on actuator performance. The results revealed a clear trade-off between compliance and stiffness: softer silicone improved bending flexibility, while stiffer silicone improved structural rigidity and load-bearing stability. These results highlight the potential of stiffness tuning to balance compliance and strength for configuring scalable, reconfigurable soft robotic arms.
Moeen Ul Islam, Cheng Ouyang, Xinda Qi +3
Department of Agricultural and Biological Engineering, Mississippi State University, MS, USA · Department of Electrical and Computer Engineering, Michigan State University, MI, USA · Texas A&M AgriLife Research and Extension Center in Dallas, Texas A&M University, TX, USA
Continuum robots enable smooth shape morphing and safe interaction in confined environments. However, most existing systems are task-specific and depend on external sensing infrastructure, limiting their adaptability and real-world deployment. This paper presents a self-contained modular continuum robotic platform that combines mechanical reconfigurability with onboard pose estimation. The robot is constructed from interchangeable continuum joints with analytically precomputed stiffness, allowing rapid assembly and direct programming of the robot shape. Proprioceptive sensing is achieved using magnetic sensors and a modular learning-based framework, where a single model is trained per joint and reused across configurations. The system is experimentally validated in real world, demonstrating self-sensing capabilities and adaptation without external tracking.
Vine-inspired robots achieve large workspace coverage through tip eversion, enabling safe navigation in confined and cluttered environments. However, their deployment in free space is fundamentally limited by low axial stiffness, poor load-bearing capacity, and the inability to retain shape during and after steering. In this work, we propose a reconfigurable pneumatic joint (RPJ) architecture that introduces discrete, pressure-tunable stiffness along the robot body without compromising continuous growth. Each RPJ module comprises symmetrically distributed pneumatic chambers that locally increase bending stiffness when pressurized, enabling decoupling between global compliance and localized rigidity. We integrate the RPJs into a soft growing robot with tendon-driven steering and develop a compact base station for mid-air eversion. System characterization and experimental validation demonstrate moderate pressure requirements for eversion, as well as comparable localized stiffening and steering performance to layer-jamming mechanisms. Demonstrations further show that the proposed robot achieves improved shape retention during bending, reduced gravitational deflection under load, cascading retraction, and reliable payload transport up to 202 g in free space. The RPJ mechanism establishes a practical pathway toward structurally adaptive vine robots for manipulation-oriented tasks such as object sorting and adaptive exploration in unconstrained environments.
Ayodele James Oyejide, Ustaz A. Yaqub, Samir Erturk +2
Department of Electrical and Electronics Engineering, Kadir Has University, Cibali, Istanbul, Türkiye · Department of Computer Engineering, Kadir Has University, Cibali, Istanbul, Türkiye · Department of Mechatronics Engineering, Istanbul Bilgi University, Istanbul, Türkiye