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1. Characterization of Compliant Parallelogram Links for 3D-Printed Delta Manipulators

   https://doi.org/10.1007/978-3-030-71151-1_7  

   Mannam, P.; Kroemer, O.; Temel, F. Z. (March 2021, International Symposium on Experimental Robotics)

   Siciliano, B.; Laschi, C.; Khatib, O. (Ed.)

   We design a compliant delta manipulator using 3D-printing and soft materials. Our design is different from the traditionally rigid delta robots as it is more accessible through low-cost 3D-printing, and can interact safely with its surroundings due to compliance. This work focuses on parallelogram links which are a key component of the delta robot design. We characterize these links over twelve dimensional parameters, such as beam and hinge thickness, and two material stiffness settings by displacing them, and observing the resulting forces and rotation angles. The parallelogram links are then integrated into a delta robot structure to test for delta mechanism behavior, which keeps the end-effector parallel to the base of the robot. We observed that using compliant hinges resulted in near-delta behavior, laying the groundwork for fabricating and utilizing 3D-printed compliant delta manipulators. 

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2. MeMo: Meaningful, Modular Controllers via Noise Injection

   Tjandrasuwita, Megan; Xu, Jie; Solar-Lezama, Armando; Matusik, Wojciech (July 2024, ICML 2024)

   Robots are often built from standardized assemblies, (e.g. arms, legs, or fingers), but each robot must be trained from scratch to control all the actuators of all the parts together. In this paper we demonstrate a new approach that takes a single robot and its controller as input and produces a set of modular controllers for each of these assemblies such that when a new robot is built from the same parts, its control can be quickly learned by reusing the modular controllers. We achieve this with a framework called MeMo which learns (Me)aningful, (Mo)dular controllers. Specifically, we propose a novel modularity objective to learn an appropriate division of labor among the modules. We demonstrate that this objective can be optimized simultaneously with standard behavior cloning loss via noise injection. We benchmark our framework in locomotion and grasping environments on simple to complex robot morphology transfer. We also show that the modules help in task transfer. On both structure and task transfer, MeMo achieves improved training efficiency to graph neural network and Transformer baselines. 

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3. Effect of variations in manufacturing and material properties on the self-folding behaviors of hydrogel and elastomer bilayer structures

   https://doi.org/10.1039/D2SM01104B  

   Zhao, Jiayu; Kazemi, Hesaneh; Kim, H. Alicia; Bae, Jinhye (November 2022, Soft Matter)

   The stimuli-responsive self-folding structure is ubiquitous in nature, for instance, the mimosa folds its leaves in response to external touch or heat, and the Venus flytrap snaps shut to trap the insect inside. Thus, modeling self-folding structures has been of great interest to predict the final configuration and understand the folding mechanism. Here, we apply a simple yet effective method to predict the folding angle of the temperature-responsive nanocomposite hydrogel/elastomer bilayer structure manufactured by 3D printing, which facilitates the study of the effect of the inevitable variations in manufacturing and material properties on folding angles by comparing the simulation results with the experimentally measured folding angles. The defining feature of our method is to use thermal expansion to model the temperature-responsive nanocomposite hydrogel rather than the nonlinear field theory of diffusion model that was previously applied. The resulted difference between the simulation and experimentally measured folding angle ( i.e. , error) is around 5%. We anticipate that our method could provide insight into the design, control, and prediction of 3D printing of stimuli-responsive shape morphing ( i.e. , 4D printing) that have potential applications in soft actuators, robots, and biomedical devices. 

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4. Robotic Antennas Using Liquid Metal Origami

   https://doi.org/10.1002/aisy.202400190  

   Mishra, Anand_K; Russo, Nicholas_E; An, Hyeon_Seok; Zekios, Constantinos_L; Georgakopoulos, Stavros_V; Shepherd, Robert_F (June 2024, Advanced Intelligent Systems)

   Two of the main challenges in origami antenna designs are creating a reliable hinge and achieving precise actuation for optimal electromagnetic (EM) performance. Herein, a waterbomb origami ring antenna is introduced, integrating the waterbomb origami principle, 3D‐printed liquid metal (LM) hinges, and robotic shape morphing. The approach, combining 3D printing, robotic actuation, and innovative antenna design, enables various origami folding patterns, enhancing both portability and EM performance. This antenna's functionality has been successfully demonstrated, displaying its communication capabilities with another antenna and its ability to navigate narrow spaces on a remote‐controlled wheel robot. The 3D‐printed LM hinge exhibits low DC resistance (200 ± 1.6 mΩ) at both flat and folded state, and, with robotic control, the antenna achieves less than 1° folding angle accuracy and a 66% folding area ratio. The antenna operates in two modes at 2.08 and 2.4 GHz, ideal for fixed mobile use and radiolocation. Through extensive simulations and experiments, the antenna is evaluated in both flat and folded states, focusing on resonant frequency, gain patterns, and hinge connectivity. The findings confirm that the waterbomb origami ring antenna consistently maintains EM performance during folding and unfolding, with stable resonant frequencies and gain patterns, proving the antenna's reliability and adaptability for use in portable and mobile devices. 

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5. Sonification of Motion of Robotic Systems with Many Degrees-of-Freedom

   https://doi.org/10.7302/25802  

   Kacem, Amal; Mohammadi, Alireza (January 2025, University of Michigan)

   Effective human-robot interaction is increasingly vital across various domains, including assistive robotics, emotional communication, entertainment, and industrial automation. Visual feedback, a common feature of current interfaces, may not be suitable for all environments. Audio feedback serves as a critical supplementary communication layer in settings where visibility is low or where robotic operations generate extensive data. Sonification, which transforms a robot's trajectory, motion, and environmental signals into sound, enhances users' comprehension of robot behavior. This improvement in understanding fosters more effective, safe, and reliable Human-Robot Interaction (HRI). Demonstrations of auditory data sonification's benefits are evident in real-world applications such as industrial assembly, robot-assisted rehabilitation, and interactive robotic exhibitions, where it promotes cooperation, boosts performance, and heightens engagement. Beyond conventional HRI environments, auditory data sonification shows substantial potential in managing complex robotic systems and intricate structures, such as hyper-redundant robots and robotic teams. These systems often challenge operators with complex joint monitoring, mathematical kinematic modeling, and visual behavior verification. This dissertation explores the sonification of motion in hyper-redundant robots and teams of industrial robots. It delves into the Wave Space Sonification (WSS) framework developed by Hermann, applying it to the motion datasets of protein molecules modeled as hyper-redundant mechanisms with numerous rigid nano-linkages. This research leverages the WSS framework to develop a sonification methodology for protein molecules' dihedral angle folding trajectories. Furthermore, it introduces a novel approach for the systematic sonification of robotic motion across varying configurations. By employing localized wave fields oriented within the robots' configuration space, this methodology generates auditory outputs with specific timbral qualities as robots move through predefined configurations or along certain trajectories. Additionally, the dissertation examines a team of wheeled industrial/service robots whose motion patterns are sonified using sinusoidal vibratory sounds, demonstrating the practical applications and benefits of this innovative approach. 

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