The size of a narrow gap traversable by a fixed-wing drone is limited by its wingspan. Inspired by birds, here, we enable the traversal of a gap of sub-wingspan width and height using a morphing-wing drone capable of temporarily sweeping in its wings mid-flight. This maneuver poses control challenges due to sudden lift loss during gap-passage at low flight speeds and the need for precisely timed wing-sweep actuation ahead of the gap. To address these challenges, we first develop an aerodynamic model for general wing-sweep morphing drone flight including low flight speeds and post-stall angles of attack. We integrate longitudinal drone dynamics into an optimal reference trajectory generation and Nonlinear Model Predictive Control framework with runtime adaptive costs and constraints. Validated on a 130 g wing-sweep-morphing drone, our method achieves an average altitude error of 5 cm during narrow-gap passage at forward speeds between 5 and 7 m/s, whilst enforcing fully swept wings near the gap across variable threshold distances. Trajectory analysis shows that the drone can compensate for lift loss during gap-passage by accelerating and pitching upwards ahead of the gap to an extent that differs between reference trajectory optimization objectives. We show that our strategy also allows for accurate gap passage on hardware whilst maintaining a constant forward flight speed reference and near-constant altitude.

An aircraft's airspeed, angle of attack, and angle of side slip are crucial to its safety, especially when flying close to the stall regime. Various solutions exist, including pitot tubes, angular vanes, and multihole pressure probes. However, current sensors are either too heavy (>30 g) or require large airspeeds (>20 m/s), making them unsuitable for small uncrewed aerial vehicles. We propose a novel multihole pressure probe, integrating sensing electronics in a single-component structure, resulting in a mechanically robust and lightweight sensor (9 g), which we released to the public domain. Since there is no consensus on two critical design parameters, tip shape (conical vs spherical) and hole spacing (distance between holes), we provide a study on measurement accuracy and noise generation using wind tunnel experiments. The sensor is calibrated using a multivariate polynomial regression model over an airspeed range of 3-27 m/s and an angle of attack/sideslip range of +/-35 deg, achieving a mean absolute error of 0.44 m/s and 0.16 deg. Finally, we validated the sensor in outdoor flights near the stall regime. Our probe enabled accurate estimations of airspeed, angle of attack and sideslip during different acrobatic manoeuvres. Due to its size and weight, this sensor will enable safe flight for lightweight, uncrewed aerial vehicles flying at low speeds close to the stall regime.

Recent advances in edible robotics and edible electronics offer great potential for novel forms of culinary art with enriched and multisensory dining experiences. However, these technologies have yet to reach our tables as truly palatable food items, mainly due to prioritized effort in functionality over flavor, lack of consistency, and use of materials that are not readily available to chefs. In this work, we gastronomically revisited two edible technologies, an edible pneumatic actuator and an edible battery, to enhance their palatability while preserving their intended functionalities. We validated these novel food items in the form of a three-tier robotic cake (RoboCake), featuring animated gummy bears and edible batteries that power decorative LED candles. RoboCake was exhibited at the 2025 World Expo in Osaka, Japan, enabling us to conduct a large-scale survey to assess consumer understanding and perception. The survey results revealed overall positive impressions and a strong willingness to eat robotic food, suggesting substantial potential for these edible technologies in gastronomy, hospitality, and food industries.

Muscle contraction is the driving mechanism of animal movement. In contrast to biological muscles, current artificial contractile actuators are not made of biodegradable and edible because they require negative pressure or complex fabrication processes. Here we present an edible contractile actuator that can be easily fabricated and operated using positive pressure. We characterize multiple edible materials with varying softness levels and analyze their influence on the actuator’s contractile motion and force output. Furthermore, we introduce a novel storage method that enhances both contraction performance and force generation. We validate the function of the proposed actuator within a partially edible linkage structure that mimics a musculoskeletal system.

Flapping-wing micro aerial vehicles offer quieter and safer operation than rotary-wing drones, yet achieving precise autonomous control of bird-scale ornithopters remains challenging: lift, airspeed, and turning authority are tightly coupled and governed by only a few control inputs. Conventional cascaded controllers treat altitude, speed, and heading independently, producing persistent tracking errors during complex maneuvers, while time-parameterized trajectory tracking requires predefined speed profiles that existing methods cannot robustly produce for these coupled dynamics. We address both limitations simultaneously with a Model Predictive Contouring Control (MPCC) approach that tracks arc-length-parameterized trajectories while optimizing progress online, eliminating the need for predefined timing. However, MPCC requires a dynamical model that captures the coupled aerodynamics without exceeding the computational budget of real-time nonlinear optimization. Here, we propose a compact, continuously differentiable model that captures the dominant couplings of bird-scale ornithopters, enabling real-time predictive control. We validated the method with the XFly ornithopter flying along circular and three-dimensional racing trajectories and achieved a mean deviation from the reference trajectory between 6.5 and 9 cm at speeds up to 3 m/s, which represents an 8.5x improvement over prior ornithopter control methods.

Wing-propelled diving birds flap their wings to move through air and water, yet the wing morphology and kinematics that enable this behavior remain poorly understood because of the difficulty of collecting in situ data. The impact of flapping frequency, wing size, and stiffness on locomotion in-and transition between-the two media are still unknown. We compared data from diving birds against experiments using a flapping-wing robot capable of flying, swimming, plunge diving, and exiting the water. We show that frequency adaptation, flexible wings, and powerful actuation enable seamless transitions without folding wings or legs, that large wings enhance flight without substantially reducing underwater efficiency, and that tail-body distance and egress angle affect water exit. These results clarify how birds (and robots) balance multifluid locomotion constraints.

While autonomous multi-robots can achieve safe and coordinated navigation, they often struggle to adapt to unforeseen conditions and to capture operator-driven objectives in unstructured environments. We present a Virtual Reality (VR)-based shared control framework for teams of drones operating in constrained and unknown environments, enabling real-time, user-guided exploration. Our approach integrates a novel user-guided motion-primitive-based planner with an admittance controller, generating dynamically feasible, collision-free trajectories while allowing the operator to flexibly influence team behavior. By leveraging user input, the framework enables the robot team to explore regions of interest that autonomous planners may overlook. The system supports mixed-reality operations with both physical and simulated drones, and implements a bilateral VR-based interface, allowing the operator to guide the robot team via migration points while receiving immediate visual feedback of the team state. Experimental results show that shared control improves obstacle avoidance, maintains inter-agent spacing, and reduces operator effort, demonstrating the feasibility and advantages of immersive, human-in-the-loop swarm navigation

Biological neural networks continuously adapt and modify themselves in response to experiences throughout their lifetime - a capability largely absent in artificial neural networks. Hebbian plasticity offers a promising path toward rapid adaptation in changing environments. Here, we introduce Hebbian Attractor Networks (HAN), a class of plastic neural networks in which local weight update normalization induces emergent attractor dynamics. Unlike prior approaches, HANs employ dual-timescale plasticity and temporal averaging of pre- and postsynaptic activations to induce either co-dynamic limit cycles or fixed-point weight attractors. Using simulated locomotion benchmarks, we gain insight into how Hebbian update frequency and activation averaging influence weight dynamics and control performance. Our results show that slower updates, combined with averaged pre- and postsynaptic activations, promote convergence to stable weight configurations, while faster updates yield oscillatory co-dynamic systems. We further demonstrate that these findings generalize to high-dimensional quadrupedal locomotion with a simulated Unitree Go1 robot. These results highlight how the timing of plasticity shapes neural dynamics in embodied systems, providing a principled characterization of the attractor regimes that emerge in self-modifying networks.

Multimodal drones combining aerial and terrestrial mobility offer adaptability and extended operational range across diverse environments. However, most existing multimodal drones rely on multiple actuators that add mass and complexity while offering limited terrestrial locomotion capabilities. Here, we introduce a multimodal winged drone driven by only a single actuator, capable of ground locomotion, flight, and ground-to-air transition by either rolling or jumping. The actuator is based on a novel transmission system that enables control of its rotational direction to switch between different locomotion modes. In one direction, the actuator drives a propeller that generates forward thrust for flight and (passive) wheeled locomotion on the ground, while in the other direction it activates a spring-leg mechanism that enables jumping by storing and releasing elastic energy. We show that the winged drone can perform fast wheeled locomotion on flat surfaces, consecutive jumps across diverse terrains, as well as take-off from a runway or jumping from a spot. Experimental characterization shows that runway take-off offers greater energy efficiency, while jumping take-off is more space-efficient and less dependent on ground conditions. The proposed actuation method enables simple and effective versatility for locomotion in diverse environments, thus extending the operational range of winged drones.

Energy production and storage represent challenges for biodegradable and edible technologies. Here, this study describes an edible energy storage and valve system designed to power pneumatically driven edible robots. The edible pneumatic battery exploits the acid-base neutralization reaction of food-grade reactants: under gravity, citric acid mixes with sodium bicarbonate powder to produce a steady release of carbon dioxide (CO2) gas. The generated gas pressure causes deformation of a connected edible pneumatic actuator. When the gas pressure reaches a threshold, an edible valve automatically releases the pressurized gas, which lets the actuator return to its resting state. The entire system, whose characteristics are consistent with model estimates, is fully edible and enables self-sustained and repetitive bending motion of the edible actuator. This design is scalable in terms of sizes (30-50 mm diameter), operation time (20-650 s), and CO2 gas generation rate (0.1-1.4 × 10-3 mol s-1). Additionally, the actuator's motion can be programmed by modifying the orifice size or the fluidic resistance between the energy source, actuator, and valve. The system is validated by fabricating a fully edible system, and its application is showcased as a foot-pressed triggered edible actuator that mimics prey behavior to attract predators.

Centimeter-scale aquatic robots could be used in environmental monitoring, exploration, and intervention in aquatic environments. However, existing robots rely on artificial polymers and commercial electronic components, which can pollute and disrupt sensitive ecological environments if they are not retrieved. To address these challenges, we describe a fully biodegradable and fully edible self-propelled device that leverages the Marangoni effect for autonomous propulsion. The body of the edible aquatic robot is made of freeze-dried fish food and is powered by a water-triggered pneumatic reaction that produces motion by sustained release of a surfactant that is safe for aquatic fauna. The device's biodegradable and non-toxic composition allows for safe environmental deployment for environmental sensing, delivery of nutrition or medication in aquatic environments. The proposed method substantially expands the potential benefits of small-scale aquatic robots that could be deployed on a large scale without the need to retrieve them and even provide nutrition to wildlife at the end of their lifetime as animals do.

Animals can finely modulate their leg stiffness to interact with complex terrains and absorb sudden shocks. In feats like leaping and sprinting, animals demonstrate a sophisticated interplay of opposing muscle pairs that actively modulate joint stiffness, while tendons and ligaments act as biological springs storing and releasing energy. Although legged robots have achieved notable progress in robust locomotion, they still lack the refined adaptability inherent in animal motor control. Integrating mechanisms that allow active control of leg stiffness presents a pathway towards more resilient robotic systems.This paper proposes a novel mechanical design to integrate compliancy into robot legs based on tensegrity - a structural principle that combines flexible cables and rigid elements to balance tension and compression. Tensegrity structures naturally allow for passive compliance, making them well-suited for absorbing impacts and adapting to diverse terrains. Our design features a robot leg with tensegrity joints and a mechanism to control the joint’s rotational stiffness by modulating the tension of the cable actuation system. We demonstrate that the robot leg can reduce the impact forces of sudden shocks by at least 34.7 % and achieve a similar leg flexion under a load difference of 10.26 N by adjusting its stiffness configuration. The results indicate that tensegrity-based leg designs harbors potential towards more resilient and adaptable legged robots.

Minimally invasive surgeries, such as cardiac ablations, increasingly resort to magnetically-steered catheters. To enhance the dexterity and tissue interaction capabilities of these devices, researchers have developed catheters with variable stiffness (VS). These devices can transition from a soft to a rigid state, or vice versa, on command by means of thermal or pressure triggers. However, the stiffness change of existing devices is either too slow (thermal-triggered) or too low and limited to only two states that prevent continuous stiffness change (pressure-triggered). Here, we propose a method for addressing the two aforementioned limitations of pressure-triggered devices.Firstly, we describe a control method for rapid and continuous stiffness change based on fiber jamming (FJ). This method allows for smooth stiffness adjustment of the catheter across more than 100 steps by regulating vacuum pressure, which affects friction force between the fibers. Secondly, we assess important design parameters of the proposed catheter, such as fiber material and roughness, to identify optimal values that increase the stiffness range up to 46%.This enables the new FJ catheter to be as safe as clinical magnetic catheters in the soft state while navigating through the body and rapidly become stiff as conventional manual catheters to achieve comparable forces onto the heart tissue.

This paper shows how different types of novel aerial robots with new functionalities can cooperate in the inspection and maintenance (I&M) of power lines, one of the largest and most essential civil infrastructures in any country. This study relies on the results from the AERIAL-CORE research and innovation project. The paper describes an I&M validation scenario and evaluation metrics for three linked operation domains: 1) long-range inspection for the detection of possible damages on power lines in a post-storm scenario, 2) aerial manipulation for the installation of devices on power lines, and 3) aerial co-working to help human operators in their activities at height. It presents the demonstration of ten different aerial robots in a real scenario with 10 km of power lines. The platforms include morphing-wing and VTOL (vertical take-off and landing) UAVs (unmanned aerial vehicles), multi-rotors, and aerial manipulators. These platforms, custom-developed or commercially available, are evaluated in the three application domains, describing the new functionalities implemented for each case. The paper ends with guidelines, design principles, and lessons learned for future developments derived from the final demonstration of the project.

Aerial swarms can substantially improve the effectiveness of drones in applications such as inspection, monitoring, and search for rescue. This is especially true when those swarms are made of several individual drones that use local sensing and coordination rules to achieve collective motion. Despite recent progress in swarm autonomy, human control and decision-making are still critical for missions where lives are at risk or human cognitive skills are required. However, first-person-view (FPV) teleoperation systems require one or more human operators per drone, limiting the scalability of these systems to swarms. This work investigates the performance, preference, and behaviour of pilots using different FPV interfaces for teleoperation of aerial swarms. Interfaces with single and multiple perspectives were experimentally studied with humans piloting a simulated aerial swarm through an obstacle course. Participants were found to prefer and perform better with views from the back of the swarm, while views from the front caused users to fly faster but resulted in more crashes. Presenting users with multiple views at once resulted in a slower completion time, and users were found to focus on the largest view, regardless of its perspective within the swarm.

Variable stiffness (VS) has revolutionized miniature surgical instruments, including cardiovascular catheters for minimally invasive surgeries (MISs), enabling advanced capabilities in stiffness modulation and multi-curvature bending. However, existing VS catheters with phase-changing materials are slow in softening and stiffening rates (≈90 s), which can lead to substantial increase in surgery duration. To address the slow stiffness change, we propose a VS catheter based on fiber jamming (FJ) that achieves instant stiffness changes (≤300 ms), enabling seamless catheter operations without delays. Moreover, our catheter, incorporating hundreds of ultrathin fibers into a slender 2.3-mm catheter body, achieves up to 6.5-fold stiffness changes. With adequate stiffness change, our two-segment catheter achieves complex bending profiles within seconds. In addition, the FJ-based design does not require electric currents or heating inside the human body, minimizing patient risks. This FJ-based VS catheter, with instantaneous response, adequate stiffness change, and enhanced safety, can potentially establish benchmarks in MIS, allowing medical practitioners to effectively address formidable diseases.

Food animation is gaining increasing attention for the ability to reduce waste and increase attractiveness in animals and humans. Although several examples of food animation methods have been recently described, the speed and range of motion are still limited. Here a method is described to design and manufacture small edible jumpers powered by rapid release of elastic energy through shell snapping. The jumping actuators are made of gelatin crosslinked with genipin and polyvinyl alcohol, ensuring resilience to stress during shell eversion. The shape and size of the shells are modeled and optimized for maximum jumping height resulting in jumpers with a diameter of 47 mm that can reach a jumping height of 361 mm. The edible jumpers can be loaded with additional nutritional components encapsulated by humidity-sensitive latches for automatic release. To showcase potential uses of such edible jumpers, a jumping food pellet for pets and an animated dessert for humans are described.

In this paper, we introduce Hebbian learning as a novel method for swarm robotics, enabling the automatic emergence of heterogeneity. Hebbian learning presents a biologically inspired form of neural adaptation that solely relies on local information. By doing so, we resolve several major challenges for learning heterogeneous control: 1) Hebbian learning removes the complexity of attributing emergent phenomena to single agents through local learning rules, thus circumventing the micro-macro problem; 2) uniform Hebbian learning rules across all swarm members limit the number of parameters needed, mitigating the curse of dimensionality with scaling swarm sizes; and 3) evolving Hebbian learning rules based on swarm-level behaviour minimises the need for extensive prior knowledge typically required for optimising heterogeneous swarms. This work demonstrates that with Hebbian learning heterogeneity naturally emerges, resulting in swarm-level behavioural switching and in significantly improved swarm capabilities. It also demonstrates how the evolution of Hebbian learning rules can be a valid alternative to Multi Agent Reinforcement Learning in standard benchmarking tasks.

Body-Machine Interfaces (BoMIs) for robotic teleoperation can improve a user’s experience and performance. However, the implementation of such systems needs to be optimized on each robot independently, as a general approach has not been proposed to date. Here, we present a novel machine learning method to generate personalized BoMIs from an operator’s spontaneous body movements. The method captures individual motor synergies that can be used for the teleoperation of robots. The proposed algorithm applies to people with diverse behavioral patterns to control robots with diverse morphologies and degrees of freedom, such as a fixed-wing drone, a quadrotor, and a robotic manipulator.