Self-reproduction is a defining property of biological life and a long-standing milestone for autonomous robotic systems. Despite advances in self-reconfiguration and algorithmic replication, no physical robot has demonstrated embodied self-reproduction capable of reversible shape transformation and structural variation. Here, we introduce a modular robotic architecture that reproduces through a geometry-guided 2D–3D–2D morphological cycle. Identical triangular modules autonomously attach, fold, and reorganize using magnetic coupling, allowing a "mother" robot to absorb free modules, increase its mass, and generate a physically detached offspring. We show that this mechanism enables reproduction across multiple scales: a minimal 2-module body reproduces with a 33% success rate in 4-module system, whereas 6-module and 8-module worms achieve 100% reproducibility across all trials. The system exhibits early signatures of differentiation, where structurally identical offspring diverge into distinct post-division states due to pathway-dependent morphological transitions. These results demonstrate that a small set of geometric, mechanical, and dynamical principles is sufficient to instantiate physical self-reproduction in a modular robot. By grounding replication in embodied interactions rather than symbolic instruction, this work provides a foundation for scalable, evolution-capable robotic lineages.

This paper presents the development of a wearable ankle rehabilitation robot based on a 3-RRR spherical parallel mechanism (SPM) to support multi-DOF recovery through pitch, roll, and yaw motions. The system features a compact, ergonomic structure designed for comfort, safety, and compatibility with ankle biomechanics. A complete design-to-dynamics pipeline has been implemented, including structural design, kinematic modeling for motion planning, and Lagrangian-based dynamic modeling for torque estimation and simulation analysis. Preliminary simulations verify stable joint coordination and smooth motion tracking under representative rehabilitation trajectories. The control framework is currently being developed to enhance responsiveness across the workspace. Future work will focus on integrating personalized modeling and adaptive strategies to address kinematic singularities through model-based control. This work establishes a foundational platform for intelligent personalized ankle rehabilitation, enabling both static training and potential extension to gait-phase-timed assistance.

Multimodal grasping has emerged as a promising strategy to enhance the grasping diversity of grippers in response to the rapid expansion of application scenarios. Among various designs, the pinch-suction hybrid mechanism and the soft-rigid hybrid structure have proved to be two practical strategies to achieve multimodality. However, existing research on these two strategies still lacks simple and effective collaborative mechanisms to fully leverage the advantages of each mode while ensuring mutual noninterference. In this article, we propose a pinch-suction and soft-rigid hybrid multimodal gripper (HMG), integrating four operating modes into a compact structure. Two simple and effective collaborative mechanisms are introduced to coordinate between pinch and suction operation and between soft and rigid components, respectively. Through the collaboration of different modes, the HMG exhibits a competitive grasping diversity across four aspects, including weight (from 0.2 g to 10 kg), fragility (from jelly to aluminum profile), size scale (from 0.46 mm to 0.55 m), and shape (from poorly pinchable to poorly suckable). We further demonstrate its adaptability and robustness in handling irregular-shaped objects, and its proficiency in executing complex real-world manipulation tasks, underwater operations, and closed-loop grasping. Its enhanced grasping diversity is poised to accelerate diverse applications in daily life, industrial settings, and underwater scenarios.

@article{zhao2025enhancing,
  author    = {Zhao, Yuwen and Zhu, Jiaqi and Zhang, Jie and Zhang, Siyuan and Shao, Maosen and Chai, Zhiping and Liu, Yimu and Wu, Jianing and Wu, Zhigang and Zhang, Jinxiu},
  title     = {Enhancing Grasping Diversity With a Pinch-Suction and Soft-Rigid Hybrid Multimodal Gripper},
  journal   = {IEEE Transactions on Robotics},
  volume    = {41},
  pages     = {3890--3907},
  year      = {2025},
}

Stiffness regulation strategies endow soft machines with stronger functionality to cope with diverse application requirements, for example manipulating heavy items by improving structural stiffness. However, most programmable stiffness strategies usually struggle to preserve the inherent compliant interaction capabilities following an enhancement in structural stiffness. In this study, inspired by the musculocutaneous system, we propose a soft stimuli-responsive material (SRM) by combining shape memory alloy into compliant materials. By characterizing the mechanical performance, the flexural modulus increases from 6.6 to 142.4 MPa under the action of active stimuli, crossing two orders of magnitude, while Young's modulus stays at 2.2 MPa during programming structural stiffness. This comparative result indicates that our SRMs can keep a lower contact stiffness for compliant interaction although structural stiffness increases. Then, we develop three diverse soft machines to show the application potential of this smart material, such as robotic grippers, wearable devices, and deployable mechanisms. By applying our materials, these machines possess stronger load-bearing capabilities. Meanwhile, these demonstrations also illustrate the efficacy of this paradigm in regulating the structural stiffness of soft machines while maintaining their compliant interaction capabilities.

@article{ma2024synergizing,
  author    = {Ma, Ke and Zhang, Jie and Sun, Ruotong and Chang, Binhan and Zhang, Siyuan and Wang, Xiaojun and Wu, Jianing and Zhang, Jinxiu},
  title     = {Synergizing Structural Stiffness Regulation with Compliance Contact Stiffness: Bioinspired Soft Stimuli-Responsive Materials Design for Soft Machines},
  journal   = {Advanced Engineering Materials},
  volume    = {26},
  number    = {18},
  pages     = {2400461},
  year      = {2024},
}

Manipulating granular materials is a crucial function of robotic grippers. However, the existing approaches always suffer from low efficiency when dealing with large quantities of dispersed granules. To overcome this challenge, inspiration is drawn from an African elephant (Loxodonta Africana), which can employ both fingertip extensions on the trunk tip to efficiently grasp dispersed granular food all at once by mediating state transition of granules. Herein, this bio-inspired intelligent strategy is integrated into a soft pneumatic gripper for transporting dispersed granules. To evaluate the critical actuation pressures while grasping granules, a library is constructed experimentally, and the effects of the initial relative height and relative lifting speed on the grasping success rate are examined. It is indicated in the experimental results that this trunk-inspired robotic strategy leads to a success rate of over 90% and saves ≈50% duration of manipulation compared to the individual gripping fashion. Herein, new insights may be offered in this study into a novel manipulation strategy for efficiently transporting dispersed granular materials.

@article{zhao2023transporting,
  author    = {Zhao, Yuwen and Zhang, Jie and Zhang, Siyuan and Zhang, Peng and Dong, Guixin and Wu, Jianing and Zhang, Jinxiu},
  title     = {Transporting Dispersed Cylindrical Granules: An Intelligent Strategy Inspired by an Elephant Trunk},
  journal   = {Advanced Intelligent Systems},
  volume    = {5},
  number    = {8},
  pages     = {2300182},
  year      = {2023},
}