Team BeAGain's development of the whole-body FES robotic bicycle and triumph at Cybathlon 2024 are presented.

Team EDAN and pilot Mattias Atzenhofer won the first Assistance Robot Race at Cybathlon 2024.

Rehab Tech's Omnia prosthesis excelled at Cybathlon, showcasing advanced lower-limb prostheses and user-centered innovation.

Quadrotors can carry slung loads to hard-to-reach locations at high speed. Given that a single quadrotor has limited payload capacities, using a team of quadrotors to collaboratively manipulate the full pose of a heavy object is a scalable and promising solution. However, existing control algorithms for multilifting systems only enable low-speed and low-acceleration operations because of the complex

Vision is a critical sensory function for humans, animals, and engineered systems, enabling environmental perception essential for imaging and autonomous operation. Although bioinspired, tunable optical systems have advanced adaptability and performance, challenges remain in achieving biocompatibility, robust yet flexible construction, and specialized multifunctionality. Here, we present a photoresponsive

DNA origami nanorobots allow for the rational design of nanomachines that respond to environmental stimuli with preprogrammed tasks. To date, this mostly is achieved by constructing two-state switches that, upon activation, change their conformation, resulting in the performance of an operation. Their applicability is often limited to a single, specific stimulus-output combination because of their

Minimally invasive interventions performed inside brain vessels with the synergistic use of microcatheters pushed over guidewires have revolutionized the way aneurysms, strokes, arteriovenous malformations, brain tumors, and other cerebrovascular conditions are being treated. However, a substantial portion of the brain vasculature remains inaccessible because the conventional catheterization technique

Acute mesenteric ischemia (AMI) results from insufficient blood flow to the intestines, leading to tissue necrosis with high morbidity and mortality. Diagnosis is often delayed because of nonspecific symptoms that mimic common gastrointestinal conditions. Current diagnostic methods, such as computed tomography and mesenteric angiography, are complex, costly, and invasive, highlighting the need for

Brains evolve within specific sensory and physical environments, yet neuroscience has traditionally focused on studying neural circuits in isolation. Understanding of their function requires integrative brain-body testing in realistic contexts. To investigate the neural and biomechanical mechanisms of sensorimotor transformations, we constructed realistic neuromechanical simulations (simZFish) of the

Tactile displays that lend tangible form to digital content could transform computing interactions. However, achieving the resolution, speed, and dynamic range needed for perceptual fidelity remains challenging. We present a dynamic tactile display that directly converts projected light into visible and tactile patterns via a photomechanical surface populated with millimeter-scale optotactile pixels

The novel Flybot gives a science-forward view of the challenges in building a fully autonomous robot fly.

The current trend toward generalist robot behaviors with monolithic artificial intelligence (AI) models is unsustainable. I advocate for a paradigm shift that embraces distributed architectures for collective robotic intelligence. A modular “mixture-of-robots” approach with specialized interdependent components can achieve superlinear gains, offering benefits in scalability, adaptability, and learning

Science laboratory automation enables accelerated discovery in life sciences and materials. However, it requires interdisciplinary collaboration to address challenges such as robust and flexible autonomy, reproducibility, throughput, standardization, the role of human scientists, and ethics. This article highlights these issues, reflecting perspectives from leading experts in laboratory automation

An extending, articulating powered wheelchair competed and won the wheelchair race at Cybathlon 2024.

Innovations in sensing and control technology helped an arm prosthesis novice win a global assistive robotics competition.

Intelligent miniature systems capable of wireless sensing and manipulation hold considerable promise for advancing biomedical applications. However, the development of these systems has been substantially hindered by sensing-actuation incompatibility at small scales. To overcome this challenge, we propose a robotic sensing approach that integrates embedded ultrasonic soft sensors (EUSSs) with magnetic

Dynamics models that predict the effects of physical interactions are essential for planning and control in robotic manipulation. Although models based on physical principles often generalize well, they typically require full-state information, which can be difficult or impossible to extract from perception data in complex, real-world scenarios. Learning-based dynamics models provide an alternative

According to productive failure (PF) theory, experiencing failure during problem-solving can enhance students’ knowledge acquisition in subsequent instruction. However, challenging students with problems beyond their current capabilities may strain their skills, prior knowledge, and emotional well-being. To address this, we designed a social robot–assisted teaching activity in which students observed

Family-centered integration is critical for the success of in-home educational robots.

Reading fluency is a vital building block for developing literacy, yet the best way to practice fluency—reading aloud—can cause anxiety severe enough to inhibit literacy development in ways that can have an adverse effect on students through adulthood. One promising intervention to mitigate oral reading anxiety is to have children read aloud to a robot. Although observations in prior work have suggested

Neuronal control of skeletal muscle function is ubiquitous across species for locomotion and doing work. In particular, emergent behaviors of neurons in biohybrid neuromuscular systems can advance bioinspired locomotion research. Although recent studies have demonstrated that chemical or optogenetic stimulation of neurons can control muscular actuation through the neuromuscular junction (NMJ), the

Modern robotic manufacturing requires collision-free coordination of multiple robots to complete numerous tasks in shared, obstacle-rich workspaces. Although individual tasks may be simple in isolation, automated joint task allocation, scheduling, and motion planning under spatiotemporal constraints remain computationally intractable for classical methods at real-world scales. Existing multiarm systems

Well-established model-based methods or good old-fashioned engineering can bootstrap learning-based robot systems.

Leading researchers debate the long-term influence of model-free methods that use large sets of demonstration data to train numerical generative models to control robots.

Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. Although traditional model-based controllers excel at planning on complex terrains, they struggle with real-world

High-resolution electronic tactile displays stand to transform haptics for remote machine operation, virtual reality, and digital information access for people who are blind or visually impaired. Yet, increasing the resolution of these displays requires increasing the number of individually addressable actuators while simultaneously reducing their total surface area, power consumption, and weight,

Robotic manipulation remains one of the most difficult challenges in robotics, with approaches ranging from classical model-based control to modern imitation learning. Although these methods have enabled substantial progress, they often require extensive manual design, struggle with performance, and demand large-scale data collection. These limitations hinder their real-world deployment at scale, where

Animals leverage their full embodiment to achieve multimodal, redundant, and subtle communication. To achieve the same for robots, they must similarly exploit their brain-body-environment interactions or their embodied intelligence. To advance this approach, we propose a framework building on Shannon’s information channel theory for communication to provide the key principles and benchmarks for advancing

Humans use diverse skills and strategies to effectively manipulate various objects, ranging from dexterous in-hand manipulation (fine motor skills) to complex whole-body manipulation (gross motor skills). The latter involves full-body engagement and extensive contact with various body parts beyond just the hands, where the compliance of our skin and muscles plays a crucial role in increasing contact

Modular microrobotics can potentially address many information-intensive microtasks in medicine, manufacturing, and the environment. However, surface area has limited the natural powering, communication, functional integration, and self-assembly of smart mass-fabricated modular robotic devices at small scales. We demonstrate the integrated self-folding and self-rolling of functionalized patterned interior

Science fiction argues for specialized robots, not general-purpose humanoid robots, for unloading of cargo and parcels.

Soft robots, with their compliant bodies, minimal environmental disturbance, and ability to withstand ambient pressures, offer promising solutions for deep-sea exploration. However, a common challenge of stiffening in soft materials impairs their effective actuation in harsh conditions. In this work, we integrated a liquid dielectric plasticizer within an electrohydraulic soft robot, serving dual critical

Exploration of lava caves on the surface of planetary bodies near Earth is of high importance for scientific research and space exploration. The natural shielding that these caves offer against radiation and small meteorites makes them well suited for preserving exobiological signatures and protecting human-made facilities. The use of a robot team arises as the safest and most cost-efficient way to

Research on robotically steerable guidewires has surged in the past decade because of their potential in addressing difficulties related to endovascular interventions. These microscale devices exhibit unique challenges in design, fabrication, and control, not necessarily present in mesoscale continuum robots such as robotic catheters and endoscopes. Existing literature on surgical robots mainly addresses

Numerous challenges in medicine could be addressed by harnessing autonomy and artificial intelligence in medical robots.

Therapeutic and assistive exoskeletons and exosuits show promise in both clinical and real-world settings. Improving their autonomy can enhance usability, effectiveness, and cost efficiency. This Review presents a generic control framework for autonomous operation of upper and lower limb devices and reviews current advancements and future directions. We highlight how data-driven machine learning aids

Most medical robots depend on human operators for sensing, decision-making, and action during procedures. Future progress depends on enabling robots to take on these capabilities. Although learning-based approaches provide remarkable promise toward achieving this goal, notable challenges must be addressed to unlock these robots’ full potential in clinical settings.

Bronchial foreign body aspiration is a life-threatening condition with a high incidence across diverse populations, requiring urgent diagnosis and treatment. However, the limited availability of skilled practitioners and advanced medical equipment in community clinics and underdeveloped regions underscores the broader challenges in emergency care. Here, we present a cost-effective robotic bronchoscope

Clifford D. Simak's Project Pope imagines a distant future where logic unifies religion.

State-of-the-art surgery is performed robotically under direct surgeon control. However, surgical outcome is limited by the availability, skill, and day-to-day performance of the operating surgeon. What will it take to improve surgical outcomes independent of human limitations? In this Review, we explore the technological evolution of robotic surgery and current trends in robotics and artificial intelligence

The integration of robotics and artificial intelligence holds promise for improving access to surgical care worldwide.

Surgical robots capable of autonomously performing various tasks could enhance efficiency and augment human productivity in addressing clinical needs. Although current solutions have automated specific actions within defined contexts, they are challenging to generalize across diverse environments in general surgery. Embodied intelligence enables general-purpose robot learning with applications for

Foundation models in robotics are here to stay, but can surgical robotics keep up with their data-intense requirement?

Safely and accurately navigating needles percutaneously or endoscopically to sites deep within the body is essential for many medical procedures, from biopsies to localized drug deliveries to tumor ablations. The advent of image guidance decades ago gave physicians information about the patient’s anatomy. We are now entering the era of AI (artificial intelligence) guidance, where AI can automatically

Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and robust generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning strategies. To address

Force-sensing capabilities are essential for robot manipulation systems. However, commonly used wrist-mounted force/torque sensors are heavy, fragile, and expensive, and tactile sensors require adding fragile circuitry to the robot fingers while only providing force information local to the contact. Here, we present a vision-based contact force estimator that serves as a more cost-effective and easier-to-implement

Microrobotic techniques are promising for treating biofilm infections located deep within the human body. However, the presence of highly viscous pus presents a formidable biological barrier, severely restricting targeted and minimally invasive treatments. In addition, conventional antibacterial agents exhibit limited payload integration with microrobotic systems, further compromising therapeutic efficiency

Although the field of wearable robotic exoskeletons is rapidly expanding, there are several barriers to entry that discourage many from pursuing research in this area, ultimately hindering growth. Chief among these is the lengthy and costly development process to get an exoskeleton from conception to implementation and the necessity for a broad set of expertise. In addition, many exoskeletons are designed

Death of the Author: A Novel imagines the influence of an experimental exoskeleton on a disabled author and her family.

The rapid development of autonomous robots has resulted in marked societal and economic benefits. However, enabling robots to navigate complex environments with human-like agility remains a formidable challenge. Unlike robots, humans excel at pathfinding because of their superior spatial awareness and their ability to leverage experience. Inspired by these observations, we designed a neural network

Neuromorphic computing offers a transformative pathway to overcome the computational and energy challenges faced in deploying robotic localization and navigation systems at the edge. Visual place recognition, a critical component for navigation, is often hampered by the high resource demands of conventional systems, making them unsuitable for small-scale robotic platforms, which still require accurate

Despite the devastating effects of pressure ulcers (PUs), little is understood about how they can be prevented using alternating-pressure (AP) mattresses. Such mattresses typically aim to minimize the pressures imparted while alternating between different states of pressure to prevent areas of tissue from being persistently occluded of blood flow. In this work, we built an actuator bed to study AP

The serious global need for on-orbit servicing of satellites and remediation of space debris demands robotic solutions.

Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, which typically relies on visual plant expression, and the manual, destructive nature of many nonvisual measurement methods, including the Scholander pressure

The human skin can reliably capture a wide range of multimodal data over a large surface while providing a soft interface. Artificial technologies using microelectromechanical systems (MEMS) can emulate these biological functions but present numerous challenges in fabrication, delamination due to soft-rigid interfaces, and electrical interference. To address these difficulties, we present a single-layer

The physical body and its interaction with the environment shape robot behavior, simplify control, and minimize computation.

Coordinating the motion between lower and upper limbs and aligning limb control with perception are substantial challenges in robotics, particularly in dynamic environments. To this end, we introduce an approach for enabling legged mobile manipulators to play badminton, a task that requires precise coordination of perception, locomotion, and arm swinging. We propose a unified reinforcement learning–based

Achieving lifelike autonomy remains a long-term aspiration, yet soft robots so far have mostly demonstrated rudimentary physical intelligence that relies on manipulation of external stimuli to generate continuous motion. To realize autonomous physical intelligence (API) capable of self-regulated sensing, decision-making, and actuation, a promising approach is creating nonlinear time-lag feedback embedded

High-speed legged navigation in discrete and geometrically complex environments is a challenging task because of the high–degree-of-freedom dynamics and long-horizon, nonconvex nature of the optimization problem. In this work, we propose a hierarchical navigation pipeline for legged robots that can traverse such environments at high speed. The proposed pipeline consists of a planner and tracker module

Physical control embodies motion intelligence in soft robots via self-regulating oscillations, sequences, and reactions.