VSL-Skin: Individually Addressable Phase-Change Voxel Skin for Variable-Stiffness and Virtual Joints Bridging Soft and Rigid Robots
This system addresses the challenge of precisely controlling stiffness distribution and creating virtual joints in robots by bridging the gap between soft and rigid robot capabilities, which is a significant problem for designers of reconfigurable robotic systems.
This paper introduces VSL-Skin, a system that provides individually addressable, voxel-level morphological control for variable stiffness in robots. It achieves nearly two orders of magnitude stiffness modulation across various modes (e.g., 15-1200 N/mm axial) with centimeter-scale precision, demonstrates 30% axial compression, and enables autonomous self-repair.
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 (8*10^2 - 3*10^4 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.