Shape-Morphing Robotics: From Fundamental Principles to Adaptive Machines

Abstract

The remarkable capability of living organisms to reshape themselves—whether the compliant deformation of musculoskeletal structure, the origami-like folding of a butterfly's wing, or the stiffness modulation of a turtle flipper—continues to inspire breakthroughs in robotic design. Nature reveals that form and function are inherently coupled and adaptability is rooted in intelligent mechanical design, e.g., materials and structures. This special issue of Advanced Intelligent Systems assembles the latest progress in shape-morphing robotics and machines that alter their physical geometry to achieve adaptive properties in response to changing environments, task demands, or unexpected disturbances.

Shape-morphing robots lie at the intersection of materials science, mechanical design, and intelligent control. Several contributions translate biological strategies directly into engineered systems. Khan et al. (doi: 10.1002/aisy.202400620) fabricate 3D-printed magnetic butterflies whose vein-embedded composites replicate monarch wing folding, delivering agile, energy-efficient aerial maneuvers. Ramirez et al. (doi: 10.1002/aisy.202401055) present JART, an amphibious robot that uses kirigami-layer jamming to toggle between flippers and legs, cutting morphing energy by approximately 98% relative to thermally driven designs. Jensen et al. (doi: 10.1002/aisy.202500422) describe DAWN, a dual-helical, wave-propelled crawler whose elastomer skins boost friction and shield internal linkages, letting it traverse sand, gravel, and wet soil. Huang et al. (doi: 10.1002/aisy.202500365) mimic human digits in a wearable pneumatic physiotherapy device that integrates actuation, sensing, and control for on-body, customizable massage. These studies showcase how biomimicry delivers both functional versatility and structural elegance, yet also highlight enduring challenges—continuous materials integration, long-term durability, and tightly coupled actuation–sensing architectures.

A recurring theme in this issue is multi-modal morphologies: the robots will tailor not only shape, but also properties, such as stiffness, for high load-bearing capabilities. In terms of shapes, Chen et al. (doi: 10.1002/aisy.202500123) achieve this through tunable-stiffness architecture morphing structures (TSAMS) that exhibit over 300-fold tunable stiffness range using shape memory alloy actuators. Samarakoon et al. (doi: 10.1002/aisy.202400417) further our understanding of the tiling robots through design, kinematic modeling and control of polyform-inspired robots capable of transforming between two polymorphic shapes, and establishing a taxonomy for scalable morphing tiling robots. Petrš et al. (doi: 10.1002/aisy.202500310) integrate McKibben muscles and elastic cords across tensegrity structures to create distributed, fault-tolerant morphing joints with variable stiffness. Demirtas et al. (doi: 10.1002/aisy.202500142) unveil Digits, a modular haptic interface whose pneumatic “fingers” tune stiffness and geometry in real time for VR and rehabilitation.

As shape-morphing robots increase in complexity and degrees of freedom, new modeling and control strategies are essential to predict, plan, and govern their high-dimensional, often nonlinear behavior. Li et al. (doi: 10.1002/aisy.202500141) provide a unifying modeling tool: a discrete-differential-geometry simulator that captures stretching, bending, and twisting of bilayer morphing structures with high fidelity and efficient computation. Wang et al. (doi: 10.1002/aisy.202400550) introduce Shape Morphing Net (SMNet), a point-cloud–driven deep-learning controller that maps arbitrary 3D targets to high-dimensional actuator commands, achieving nearly 98% reproduction accuracy across multiple actuator technologies. Huang et al. (doi: 10.1002/aisy.202500365) implement a hybrid control system combining feedforward inverse dynamics (based on an empirical pressure–force model) with PID feedback from thin-film sensors to regulate massage force over acupoints. Demirtas et al. (doi: 10.1002/aisy.202500142) propose a modular control architecture for their haptic platform. Local pressure is controlled per module, and minimal sensing is interpreted via lightweight machine learning models to infer stiffness and interaction states.

A common thread across the papers in this issue is the move from single, centralized motors toward spatially distributed actuation paired with in-situ sensing, yielding richer deformation vocabularies and finer closed-loop control. Khan et al. (doi: 10.1002/aisy.202400620) embed magnetized veins throughout each wing panel; every segment contributes a micro-torque, allowing smooth bending waves and passive shape recovery without bulky servos. Ramirez et al. (doi: 10.1002/aisy.202401055) combine kirigami skins with multiple vacuum-jamming chambers and fiber-reinforced pneumatic bladders; by tuning pressure locally, the robot independently shapes each limb. Jensen et al. (doi: 10.1002/aisy.202500422) distribute contact forces along DAWN's body via dual helices under an elastomeric skin, improving terrain compliance and fault tolerance. Chen et al. (doi: 10.1002/aisy.202500123) place shape-memory-alloy springs between pairs of tessellated particles, enabling gradient-controlled curvature and local stiffness tuning. Demirtas et al. (doi: 10.1002/aisy.202500142) and Huang et al. (doi: 10.1002/aisy.202500365) embed soft sensors beneath pneumatic actuators for localized state feedback and adaptive control. Samarakoon et al. (doi: 10.1002/aisy.202400417) design hinge placement across tiling units to enable sequential morphological transitions. Wang et al. (doi: 10.1002/aisy.202400550) treat actuator arrays as a high-dimensional control surface, while Li et al. (doi: 10.1002/aisy.202500141) model strain mismatches in bilayer systems to drive global deformations. Together, these systems reflect a paradigm shift: actuation and sensing are no longer appended but deeply embedded throughout the robots’ structures, offering new frontiers in adaptability, robustness, and intelligence.

By integrating insights from biology with advances in smart materials, fabrication, and learning-based control, the contributions in this issue push shape-morphing robotics toward truly adaptive, resilient, and multifunctional machines. We anticipate that the principles and platforms showcased here will catalyze the next generation of robots capable not just of motion, but of continual self-reconfiguration in the face of an ever-changing world.