Our muscles are nature's actuators. The sinewy tissue is what generates the forces that make our bodies move. In recent years, engineers have used real muscle tissue to actuate "biohybrid robots" made from both living tissue and synthetic parts. By pairing lab-grown muscles with synthetic skeletons, researchers are engineering a menagerie of muscle-powered crawlers, walkers, swimmers, and grippers. But for the most part, these designs are limited in the amount of motion and power they can produce.

In recent years, many countries, including Japan, have rapidly aging populations, making the preservation of seniors' quality of life a significant concern. For elderly people with impaired physical abilities, support for toileting is one of the most important issues. This paper details the design, development, experimental assessment, and potential application of the gripper system, with a focus on the unique requirements and obstacles involved in aiding elderly or hemiplegic individuals in dressing and undressing trousers. The gripper we propose seeks to find the right balance between compliance and grasping forces, ensuring precise manipulation while maintaining a safe and compliant interaction with the users. The gripper's integration into a custom--built robotic manipulator system provides a comprehensive solution for assisting hemiplegic individuals in their dressing and undressing tasks. Experimental evaluations and comparisons with existing studies demonstrate the gripper's ability to successfully assist in both dressing and dressing of trousers in confined spaces with a high success rate. This research contributes to the advancement of assistive robotics, empowering elderly, and physically impaired individuals to maintain their independence and improve their quality of life.

At only 16, Jared Lepora has also co-authored a paper. Breakthroughs, discoveries, and DIY tips sent every weekday. In October, a student presented a robotic hand made entirely from LEGOs at the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems in Hangzhou, China. Nonetheless, the 16-year-old co-authored research recently published on arXiv along with colleagues including his father Nathan Lepora, a professor of robotics and artificial intelligence at the University of Bristol. Jared used LEGO MINDSTORMS, a LEGO robotics kit, to build a LEGO version of SoftHand-A, a 3D-printed anthropomorphic robot hand introduced in an earlier study .

Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering path planning and obstacle avoidance. This paper presents a complete, open-source, and reproducible system that enables navigation for a 3-bar tensegrity robot. The system comprises: (i) an inexpensive, open-source hardware design, and (ii) an integrated, open-source software stack for physics-based modeling, system identification, state estimation, path planning, and control. All hardware and software are publicly available at https://sites.google.com/view/tensegrity-navigation/. The proposed system tracks the robot's pose and executes collision-free paths to a specified goal among known obstacle locations. System robustness is demonstrated through experiments involving unmodeled environmental challenges, including a vertical drop, an incline, and granular media, culminating in an outdoor field demonstration. To validate reproducibility, experiments were conducted using robot instances at two different laboratories. This work provides the robotics community with a complete navigation system for a compliant, impact-resistant, and shape-morphing robot. This system is intended to serve as a springboard for advancing the navigation capabilities of other unconventional robotic platforms.

Endoscopic endonasal approaches (EEA) have become more prevalent for minimally invasive skull base and sinus surgeries. However, rigid scopes and tools significantly decrease the surgeon's ability to operate in tight anatomical spaces and avoid critical structures such as the internal carotid artery and cranial nerves. This paper proposes a novel tendon-actuated concentric tube endonasal robot (TACTER) design in which two tendon-actuated robots are concentric to each other, resulting in an outer and inner robot that can bend independently. The outer robot is a unidirectionally asymmetric notch (UAN) nickel-titanium robot, and the inner robot is a 3D-printed bidirectional robot, with a nickel-titanium bending member. In addition, the inner robot can translate axially within the outer robot, allowing the tool to traverse through structures while bending, thereby executing follow-the-leader motion. A Cosserat-rod based mechanical model is proposed that uses tendon tension of both tendon-actuated robots and the relative translation between the robots as inputs and predicts the TACTER tip position for varying input parameters. The model is validated with experiments, and a human cadaver experiment is presented to demonstrate maneuverability from the nostril to the sphenoid sinus. This work presents the first tendon-actuated concentric tube (TACT) dexterous robotic tool capable of performing follow-the-leader motion within natural nasal orifices to cover workspaces typically required for a successful EEA.

Abstract-- T endon-actuated concentric tube mechanisms combine the advantages of tendon-driven continuum robots and concentric tube robots while addressing their respective limitations. They overcome the restricted degrees of freedom often seen in tendon-driven designs, and mitigate issues such as snapping instability associated with concentric tube robots. However, a complete and general mechanical model for these systems remains an open problem. The model allows each tube to twist and elongate while enforcing a shared centerline for bending. We validate the proposed framework through experiments with two-tube and three-tube assemblies under various tendon routing configurations, achieving tip prediction errors < 4% of the robot's total length. We further demonstrate the model's generality by applying it to existing robots in the field, where maximum tip deviations remain around 5% of the total length. This model provides a foundation for accurate shape estimation and control of advanced tendon-actuated concentric tube robots. Minimally invasive surgical interventions have revolutionized modern medicine by reducing patient trauma, shortening recovery times, and improving procedural outcomes. However, accessing deep-seated anatomical targets, such as the spine, brain, or vasculature, poses significant challenges due to the confined, and deformable nature of biological tissues. While highly accurate in structured environments, traditional rigid-link robotic systems often lack the flexibility and compliance required to safely navigate these constrained anatomical spaces.

Amazon FAR (Frontier AI & Robotics)Figure 1: We present a Cross-Entropy Method (CEM) with reward model (CEM-RM) framework that optimizes block-wise, finger-wise, and tendon-routing design distributions of a soft robotic hand using pre-collected teleoperation data. Hardware experiments demonstrate that CEM-RM achieves effective design optimization with significantly fewer samples than pure optimization, enabling robust grasping of challenging objects. Abstract-- Soft robotic hands promise to provide compliant and safe interaction with objects and environments. However, designing soft hands to be both compliant and functional across diverse use cases remains challenging. Although co-design of hardware and control better couples morphology to behavior [1], the resulting search space is high-dimensional, and even simulation-based evaluation is computationally expensive. In this paper, we propose a Cross-Entropy Method with Reward Model (CEM-RM) framework that efficiently optimizes tendon-driven soft robotic hands based on teleoperation control policy, reducing design evaluations by more than half compared to pure optimization while learning a distribution of optimized hand designs from pre-collected teleoperation data. We derive a design space for a soft robotic hand composed of flexural soft fingers and implement parallelized training in simulation.

Abstract-- This paper introduces an anthropomorphic robot hand built entirely using LEGO MINDSTORMS: the Educational SoftHand-A, a tendon-driven, highly-underactuated robot hand based on the Pisa/IIT SoftHand and related hands. T o be suitable for an educational context, the design is constrained to use only standard LEGO pieces with tests using common equipment available at home. The hand features dual motors driving an agonist/antagonist opposing pair of tendons on each finger, which are shown to result in reactive fine control. The finger motions are synchonized through soft synergies, implemented with a differential mechanism using clutch gears. Altogether, this design results in an anthropomorphic hand that can adaptively grasp a broad range of objects using a simple actuation and control mechanism. Since the hand can be constructed from LEGO pieces and uses state-of-the-art design concepts for robotic hands, it has the potential to educate and inspire children to learn about the frontiers of modern robotics.

Abstract-- This paper presents the SABD hand, a 16-degree-of-freedom (DoF) robotic hand that departs from purely anthropomorphic designs to achieve an expanded grasp envelope, enable manipulation poses beyond human capability, and reduce the required number of actuators. This is achieved by combining the adduction/abduction (Add/Abd) joint of digits four and five into a single joint with a large range of motion. The combined joint increases the workspace of the digits by 400% and reduces the required DoFs while retaining dexterity. Experimental results demonstrate that the combined Add/Abd joint enables the hand to grasp objects with a side distance of up to 200 mm. Reinforcement learning-based investigations show that the design enables grasping policies that are effective not only for handling larger objects but also for achieving enhanced grasp stability. In teleoperated trials, the hand successfully performed 86% of attempted grasps on suitable YCB objects, including challenging non-anthropomorphic configurations. These findings validate the design's ability to enhance grasp stability, flexibility, and dexterous manipulation without added complexity, making it well-suited for a wide range of applications. A. Motivation Robust grasping for robotic manipulation is one of the key issues preventing the usage of robots in many applications [1]. The difficulty herein can be attributed to both software [2] and hardware challenges [3]. No robotic manipulator has been able to fully match the dexterity, power-to-weight ratio, and exteroception of the human hand [4]. Commercially available solutions, such as robotic grippers [5], the Shadow Robotic Hand [6], the Allegro Hand [7] and the Leap Hand [8], tend to be expensive or overly limited in their capabilities.

General-purpose robots should possess human-like dexterity and agility to perform tasks with the same versatility as us. A human-like form factor further enables the use of vast datasets of human-hand interactions. However, the primary bottleneck in dexterous manipulation lies not only in software but arguably even more in hardware. Robotic hands that approach human capabilities are often prohibitively expensive, bulky, or require enterprise-level maintenance, limiting their accessibility for broader research and practical applications. What if the research community could get started with reliable dexterous hands within a day? We present the open-source ORCA hand, a reliable and anthropomorphic 17-DoF tendon-driven robotic hand with integrated tactile sensors, fully assembled in less than eight hours and built for a material cost below 2,000 CHF. We showcase ORCA's key design features such as popping joints, auto-calibration, and tensioning systems that significantly reduce complexity while increasing reliability, accuracy, and robustness. We benchmark the ORCA hand across a variety of tasks, ranging from teleoperation and imitation learning to zero-shot sim-to-real reinforcement learning. Furthermore, we demonstrate its durability, withstanding more than 10,000 continuous operation cycles - equivalent to approximately 20 hours - without hardware failure, the only constraint being the duration of the experiment itself. Video is here: https://youtu.be/kUbPSYMmOds. Design files, source code, and documentation are available at https://srl.ethz.ch/orcahand.