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The tight integration of actuation, sensing, and communication capabilities into origami robots enables the development of new-generation functional robots. However, this task is challenging because the conventional materials (e.g., papers and plastics) for building origami robots lack design opportunities for incorporating add-on functionalities. Installing external electronics requires high system integration and inevitably increases the robotic weight. Here, a graphene oxide (GO)–enabled templating synthesis was developed to produce reconfigurable, compliant, multifunctional metallic backbones for the fabrication of origami robots with built-in strain sensing and wireless communication capabilities. The GO-enabled templating synthesis realized the production of complex noble metal origamis (such as Pt) with high structural replication of their paper templates. The reproduced Pt origami structures were further stabilized with thin elastomer, and the Pt-elastomer origamis were reconfigurable and served as the multifunctional backbones for building origami robots. Compared with traditional paper and plastic materials, the reconfigurable Pt backbones were more deformable, fire retardant, and power efficient. In addition, the robots with conductive Pt-elastomer backbones (Pt robots) demonstrated distinct capabilities—such as on-demand resistive heating, strain sensing, and built-in antennas—without the need for external electronics. The multifunctionality of Pt robots was further demonstrated to extend beyond the capabilities of traditional paper-based robots, such as melting an ice cube to escape, monitoring/recording robotic motions in real time, and wireless communications between robots. The development of multifunctional metallic backbones that couple actuation, sensing, and communication enriches the material library for the fabrication of soft robotics toward high functional integration. [ABSTRACT FROM AUTHOR]

Living tissues are still far from being used as practical components in biohybrid robots because of limitations in life span, sensitivity to environmental factors, and stringent culture procedures. Here, we introduce fungal mycelia as an easy-to-use and robust living component in biohybrid robots. We constructed two biohybrid robots that use the electrophysiological activity of living mycelia to control their artificial actuators. The mycelia sense their environment and issue action potential–like spiking voltages as control signals to the motors and valves of the robots that we designed and built. The paper highlights two key innovations: first, a vibration- and electromagnetic interference–shielded mycelium electrical interface that allows for stable, long-term electrophysiological bioelectric recordings during untethered, mobile operation; second, a control architecture for robots inspired by neural central pattern generators, incorporating rhythmic patterns of positive and negative spikes from the living mycelia. We used these signals to control a walking soft robot as well as a wheeled hard one. We also demonstrated the use of mycelia to respond to environmental cues by using ultraviolet light stimulation to augment the robots' gaits. Editor's summary: Many biohybrid robots are powered by animal or plant cells, which are sensitive to specific culture procedures and limited to short life spans. In contrast, fungi can be easily cultured and are robust in extreme conditions. Taking advantage of fungal mycelia's natural light sensitivity, Mishra et al. developed an electrical interface to both house the mycelia and measure their electrophysiological action potentials. A control model was then developed to use the rhythmic voltage spikes from the living mycelia to control the locomotion of both a soft starfish-inspired robot and a wheeled robot. Robot trajectories could be altered by stimulating the mycelia with ultraviolet light. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Robotics is a forward-looking discipline. Attention is focused on identifying the next grand challenges. In an applied field such as medical robotics, however, it is important to plan the future based on a clear understanding of what the research community has recently accomplished and where this work stands with respect to clinical needs and commercialization. This Review article identifies and analyzes the eight key research themes in medical robotics over the past decade. These thematic areas were identified using search criteria that identified the most highly cited papers of the decade. Our goal for this Review article is to provide an accessible way for readers to quickly appreciate some of the most exciting accomplishments in medical robotics over the past decade; for this reason, we have focused only on a small number of seminal papers in each thematic area. We hope that this article serves to foster an entrepreneurial spirit in researchers to reduce the widening gap between research and translation. [ABSTRACT FROM AUTHOR]

Lumbar spine injuries resulting from heavy or repetitive lifting remain a prevalent concern in workplaces. Back-support devices have been developed to mitigate these injuries by aiding workers during lifting tasks. However, existing devices often fall short in providing multidimensional force assistance for asymmetric lifting, an essential feature for practical workplace use. In addition, validation of device safety across the entire human spine has been lacking. This paper introduces the Bilateral Back Extensor Exosuit (BBEX), a robotic back-support device designed to address both functionality and safety concerns. The design of the BBEX draws inspiration from the anatomical characteristics of the human spine and back extensor muscles. Using a multi–degree-of-freedom architecture and serially connected linear actuators, the device's components are strategically arranged to closely mimic the biomechanics of the human spine and back extensor muscles. To establish the efficacy and safety of the BBEX, a series of experiments with human participants was conducted. Eleven healthy male participants engaged in symmetric and asymmetric lifting tasks while wearing the BBEX. The results confirm the ability of the BBEX to provide effective multidimensional force assistance. Moreover, comprehensive safety validation was achieved through analyses of muscle fatigue in the upper and the lower erector spinae muscles, as well as mechanical loading on spinal joints during both lifting scenarios. By seamlessly integrating functionality inspired by human biomechanics with a focus on safety, this study offers a promising solution to address the persistent challenge of preventing lumbar spine injuries in demanding work environments. Editor's summary: Repetitive tasks involving lifting of objects such as in industrial settings can cause injuries to the spine and back muscles. Although wearable exoskeleton suits can be used in these settings to alleviate risks to injury, they may not provide multidimensional movement during asymmetric lifting. Kim et al. have developed an active Bilateral Back Extensor Exosuit capable of multidimensional force assistance during lifting tasks. The wearable device offers multiple degrees of freedom in range of motion and was shown to provide back muscle force assistance and to decrease compression on the spines of human participants during asymmetric and symmetric lifting tasks. —Amos Matsiko [ABSTRACT FROM AUTHOR]

This paper introduces an approach to fabricating lightweight, untethered soft robots capable of diverse biomimetic locomotion. Untethering soft robotics from electrical or pneumatic power remains one of the prominent challenges within the field. The development of functional untethered soft robotic systems hinges heavily on mitigating their weight; however, the conventional weight of pneumatic network actuators (pneu-nets) in soft robots has hindered untethered operations. To address this challenge, we developed film-balloon (FiBa) modules that drastically reduced the weight of soft actuators. FiBa modules combine transversely curved polymer thin films and three-dimensionally printed pneumatic balloons to achieve varied locomotion modes. These lightweight FiBa modules serve as building blocks to create untethered soft robots mimicking natural movement strategies. These modules substantially reduce overall robot weight, allowing the integration of components such as pumps, valves, batteries, and control boards, thereby enabling untethered operation. FiBa modules integrated with electronic components demonstrated four bioinspired modes of locomotion, including turtle-inspired crawling, inchworm-inspired climbing, bat-inspired perching, and ladybug-inspired flying. Overall, our study offers an alternative tool for designing and customizing lightweight, untethered soft robots with advanced functionalities. The reduction of the weight of soft robots enabled by our approach opens doors to a wide range of applications, including disaster relief, space exploration, remote sensing, and search and rescue operations, where lightweight, untethered soft robotic systems are essential. Editor's summary: Many soft robots rely on external electrical or pneumatic power sources accompanied by a physical cumbersome tether limiting the robots' range. To achieve untethered operation, Ching et al. combined a three-dimensionally printed pneumatic balloon and a curved polymer film to form a soft actuator termed film-balloon, or FiBa, actuator. These bending actuators are substantially lighter than other pneumatic actuators and can be combined with onboard pneumatic pumps, batteries, and controllers for untethered operation. The versatility of these actuators is demonstrated in various biomimetic robot designs that can crawl, climb, perch, and fly. Untethered robots capable of complex modes of locomotion could prove useful in remote operations such as rescue missions and space exploration. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Existing robotic systems have a tension between generality and precision. Deployed solutions for robotic manipulation tend to fall into the paradigm of one robot solving a single task, lacking "precise generalization," or the ability to solve many tasks without compromising on precision. This paper explores solutions for precise and general pick and place. In precise pick and place, or kitting, the robot transforms an unstructured arrangement of objects into an organized arrangement, which can facilitate further manipulation. We propose SimPLE (Simulation to Pick Localize and placE) as a solution to precise pick and place. SimPLE learns to pick, regrasp, and place objects given the object's computer-aided design model and no prior experience. We developed three main components: task-aware grasping, visuotactile perception, and regrasp planning. Task-aware grasping computes affordances of grasps that are stable, observable, and favorable to placing. The visuotactile perception model relies on matching real observations against a set of simulated ones through supervised learning to estimate a distribution of likely object poses. Last, we computed a multistep pick-and-place plan by solving a shortest-path problem on a graph of hand-to-hand regrasps. On a dual-arm robot equipped with visuotactile sensing, SimPLE demonstrated pick and place of 15 diverse objects. The objects spanned a wide range of shapes, and SimPLE achieved successful placements into structured arrangements with 1-mm clearance more than 90% of the time for six objects and more than 80% of the time for 11 objects. Editor's summary: In robotic manipulation, there is often a trade-off between high accuracy for a repetitive motion and reliability in an unstructured environment. To teach a robot to move objects into an organized arrangement, Bauza et al. have developed a framework called SimPLE, which stands for Simulation to Pick, Localize, and placE. Given only a model of the object, the framework generates training data by sampling grasps in simulation. The SimPLE framework was tested with a set of 15 objects of different geometries on a dual-arm robot equipped with tactile sensors and an external depth camera. Using hand-to-hand regrasps, the robot successfully relocated the objects into structured arrangements, demonstrating the possibility of transferring a model learned in simulation to a real robot. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

NASA's Perseverance rover uses robotic autonomy to achieve its mission goals on Mars. Its self-driving autonomous navigation system (AutoNav) has been used to evaluate 88% of the 17.7-kilometer distance traveled during its first Mars year of operation. Previously, the maximum total autonomous distance evaluated was 2.4 kilometers by the Opportunity rover during its 14-year lifetime. AutoNav has set multiple planetary rover records, including the greatest distance driven without human review (699.9 meters) and the greatest single-day drive distance (347.7 meters). The Autonomous Exploration for Gathering Increased Science (AEGIS) system analyzes wide-angle imagery onboard to autonomously select targets for observations by the SuperCam instrument, a multimode sensor suite capable of millimeter-scale geochemical and mineralogical analysis. AEGIS enables observations of scientifically interesting targets during or immediately after long drives without the need for ground communication. OnBoard Planner (OBP) is a scheduling capability planned for operational use in September 2023 that has the potential to reduce energy usage by up to 20% and complete drive and arm-contact science campaigns in 25% fewer days on Mars. This paper presents an overview of the AutoNav, AEGIS, and OBP capabilities used on Perseverance. [ABSTRACT FROM AUTHOR]

We present an avatar system designed to facilitate the embodiment of humanoid robots by human operators, validated through iCub3, a humanoid developed at the Istituto Italiano di Tecnologia. More precisely, the paper makes two contributions: First, we present the humanoid iCub3 as a robotic avatar that integrates the latest significant improvements after about 15 years of development of the iCub series. Second, we present a versatile avatar system enabling humans to embody humanoid robots encompassing aspects such as locomotion, manipulation, voice, and facial expressions with comprehensive sensory feedback including visual, auditory, haptic, weight, and touch modalities. We validated the system by implementing several avatar architecture instances, each tailored to specific requirements. First, we evaluated the optimized architecture for verbal, nonverbal, and physical interactions with a remote recipient. This testing involved the operator in Genoa and the avatar in the Biennale di Venezia, Venice—about 290 kilometers away—thus allowing the operator to visit the Italian art exhibition remotely. Second, we evaluated the optimized architecture for recipient physical collaboration and public engagement on stage, live, at the We Make Future show, a prominent world digital innovation festival. In this instance, the operator was situated in Genoa while the avatar operated in Rimini—about 300 kilometers away—interacting with a recipient who entrusted the avatar with a payload to carry on stage before an audience of approximately 2000 spectators. Third, we present the architecture implemented by the iCub Team for the All Nippon Airways (ANA) Avatar XPrize competition. [ABSTRACT FROM AUTHOR]

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control. [ABSTRACT FROM AUTHOR]

Soft robot arms offer safety and adaptability due to their passive compliance, but this compliance typically limits their payload capacity and prevents them from performing many tasks. This paper presents a model-based design approach to effectively increase the payload capacity of soft robot arms. The proposed approach uses localized body stiffening to decrease the compliance at the end effector without sacrificing the robot's range of motion. This approach is validated on both a simulated and a real soft robot arm, where experiments show that increasing the stiffness of localized regions of their bodies reduces the compliance at the end effector and increases the height to which the arm can lift a payload. By increasing the payload capacity of soft robot arms, this approach has the potential to improve their efficacy in a variety of tasks including object manipulation and exploration of cluttered environments. [ABSTRACT FROM AUTHOR]

We propose a dual-resolution scheme to achieve time-efficient autonomous exploration with one or many robots. The scheme maintains a high-resolution local map of the robot's immediate vicinity and a low-resolution global map of the remaining areas of the environment. We believe that the strength of our approach lies in this low- and high-resolution representation of the environment: The high-resolution local map ensures that the robots observe the entire region in detail, and because the local map is bounded, so is the computation burden to process it. The low-resolution global map directs the robot to explore the broad space and only requires lightweight computation and low bandwidth to communicate among the robots. This paper shows the strength of this approach for both single-robot and multirobot exploration. For multirobot exploration, we also introduce a "pursuit" strategy for sharing information among robots with limited communication. This strategy directs the robots to opportunistically approach each other. We found that the scheme could produce exploration paths with a bounded difference in length compared with the theoretical shortest paths. Empirically, for single-robot exploration, our method produced 80% higher time efficiency with 50% lower computational runtimes than state-of-the-art methods in more than 300 simulation and real-world experiments. For multirobot exploration, our pursuit strategy demonstrated higher exploration time efficiency than conventional strategies in more than 3400 simulation runs with up to 20 robots. Last, we discuss how our method was deployed in the DARPA Subterranean Challenge and demonstrated the fastest and most complete exploration among all teams. [ABSTRACT FROM AUTHOR]

Social robots hold great promise as companions and peer learners for children, yet little is known about how they can be best designed for this population, what interaction scenarios can benefit from their use, and how they might fit into learning activities and environments. We aimed to close this gap by designing a learning-companion robot to augment guided reading activity and examined the robot's impact on an in-home reading experience. In this paper, we compared the experiences of early adolescent children aged 10 to 12 years (N = 24) who completed guided reading activities either with a learning-companion robot or as a paper-based activity in a 2-week-long, in-home field study. We found similar reading frequency and duration in both conditions and that both guided reading activities were described as positive experiences that helped to build reading skill and to sustain engagement. Children who read with the learning-companion robot further reported that the activities supported reading comprehension and motivated them to read and indicated a deepening social connection (i.e., companionship or affiliation) with the robot. We conclude that, rather than the activity falling off after a novelty effect, our simple prototype social robot is capable of preserving the benefits of an existing in-home learning activity while transforming the reading experience into a valuable, social one. Our findings contribute to an understanding of how we might capitalize on the capacity of social robots to serve as a transformative learning tool as robots become more widely available to the public. [ABSTRACT FROM AUTHOR]

The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot's internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines. [ABSTRACT FROM AUTHOR]

Social robots must take on many roles when interacting with people in everyday settings, some of which may be authoritative, such as a nurse, teacher, or guard. It is important to investigate whether and how authoritative robots can influence people in applications ranging from health care and education to security and in the home. Here, we present a human-robot interaction study that directly investigates the effect of a robot's peer or authority role (formal authority) and control of monetary rewards and penalties (real authority) on its persuasive influence. The study consisted of a social robot attempting to persuade people to change their answers to the robot's suggestion in a series of challenging attention and memory tasks. Our results show that the robot in a peer role was more persuasive than when in an authority role, contrary to expectations from human-human interactions. The robot was also more persuasive when it offered rewards over penalties, suggesting that participants perceived the robot's suggestions as a less risky option than their own estimates, in line with prospect theory. In general, the results show an aversion to the persuasive influence of authoritative robots, potentially due to the robot's legitimacy as an authority figure, its behavior being perceived as dominant, or participant feelings of threatened autonomy. This paper explores the importance of persuasion for robots in different social roles while providing critical insight into the perception of robots in these roles, people's behavior around these robots, and the development of human-robot relationships. [ABSTRACT FROM AUTHOR]

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments such as homes or hospitals. Many learning-based approaches have been proposed in response to the lack of semantic understanding of the classical pipeline for spatial navigation, which builds a geometric map using depth sensors and plans to reach point goals. Broadly, end-to-end learning approaches reactively map sensor inputs to actions with deep neural networks, whereas modular learning approaches enrich the classical pipeline with learning-based semantic sensing and exploration. However, learned visual navigation policies have predominantly been evaluated in sim, with little known about what works on a robot. We present a large-scale empirical study of semantic visual navigation methods comparing representative methods with classical, modular, and end-to-end learning approaches across six homes with no prior experience, maps, or instrumentation. We found that modular learning works well in the real world, attaining a 90% success rate. In contrast, end-to-end learning does not, dropping from 77% sim to a 23% real-world success rate because of a large image domain gap between sim and reality. For practitioners, we show that modular learning is a reliable approach to navigate to objects: Modularity and abstraction in policy design enable sim-to-real transfer. For researchers, we identify two key issues that prevent today's simulators from being reliable evaluation benchmarks—a large sim-to-real gap in images and a disconnect between sim and real-world error modes—and propose concrete steps forward. [ABSTRACT FROM AUTHOR]

The heart's intricate myocardial architecture has been called the Gordian knot of anatomy, an impossible tangle of intricate muscle fibers. This complexity dictates equally complex cardiac motions that are difficult to mimic in physical systems. If these motions could be generated by a robotic system, then cardiac device testing, cardiovascular disease studies, and surgical procedure training could reduce their reliance on animal models, saving time, costs, and lives. This work introduces a bioinspired soft robotic left ventricle simulator capable of reproducing the minutiae of cardiac motion while providing physiological pressures. This device uses thin-filament artificial muscles to mimic the multilayered myocardial architecture. To demonstrate the device's ability to follow the cardiac motions observed in the literature, we used canine myocardial strain data as input signals that were subsequently applied to each artificial myocardial layer. The device's ability to reproduce physiological volume and pressure under healthy and heart failure conditions, as well as effective simulation of a cardiac support device, were experimentally demonstrated in a left-sided mock circulation loop. This work also has the potential to deliver faithful simulated cardiac motion for preclinical device and surgical procedure testing, with the potential to simulate patient-specific myocardial architecture and motion. Editor's summary: Cardiac devices and interventions are often dependent on the use of animal models and ex vivo tissue for testing and development. Moreover, surgical training for procedure-specific skills also relies on animal tissue. Davies et al. have developed a soft robotic device that can mimic the left ventricle of the heart and reproduce cardiac motion as well as physiological hemodynamic conditions. They demonstrated the ability to simulate conditions of healthy and diseased heart states, with the potential to counter the need for animal models to develop cardiac devices. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Biological motions of native muscle tissues rely on the nervous system to interface movement with the surrounding environment. The neural innervation of muscles, crucial for regulating movement, is the fundamental infrastructure for swiftly responding to changes in body tissue requirements. This study introduces a bioelectronic neuromuscular robot integrated with the motor nervous system through electrical synapses to evoke cardiac muscle activities and steer robotic motion. Serving as an artificial brain and wirelessly regulating selective neural activation to initiate robot fin motion, a wireless frequency multiplexing bioelectronic device is used to control the robot. Frequency multiplexing bioelectronics enables the control of the robot locomotion speed and direction by modulating the flapping of the robot fins through the wireless motor innervation of cardiac muscles. The robots demonstrated an average locomotion speed of ~0.52 ± 0.22 millimeters per second, fin-flapping frequency up to 2.0 hertz, and turning locomotion path curvature of ~0.11 ± 0.04 radians per millimeter. These systems will contribute to the expansion of biohybrid machines into the brain-to-motor frontier for developing autonomous biohybrid systems capable of advanced adaptive motor control and learning. Editor's summary: Typically, biohybrid machines built from cardiac muscle cells have relied on the involuntary, contractile actuation of the cells to continuously propel the robot. However, without the nervous system, this rhythmic pumping action could not be adjusted or controlled. To imbue control, Tetsuka et al. developed a biohybrid swimmer with a neurocardiac muscle junction. Acting as an artificial brain, a frequency multiplexing wireless system was used to stimulate the motor neurons, which in turn activated the cardiac muscle cells. The robot's speed could be controlled by modulating the frequency and pulse duration of the electrical signal, and direction was altered by transmitting signals to the left and right fins independently. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

The sense of touch is a property that allows humans to interact delicately with their physical environment. This article reports on a technological advancement in intuitive human-robot interaction that enables an intrinsic robotic sense of touch without the use of artificial skin or tactile instrumentation. On the basis of high-resolution joint-force-torque sensing in a redundant arrangement, we were able to let the robot sensitively feel the surrounding environment and accurately localize touch trajectories in space and time that were applied on its surface by a human. Through an intertwined combination of manifold learning techniques and artificial neural networks, the robot identified and interpreted those touch trajectories as machine-readable letters, symbols, or numbers. This opens up unexplored opportunities in terms of intuitive and flexible interaction between human and robot. Furthermore, we showed that our concept of so-called virtual buttons can be used to straightforwardly implement a tactile communication link, including switches and slider bars, which are complementary to speech, hardware buttons, and control panels. These interaction elements could be freely placed, moved, and configured in arbitrary locations on the robot structure. The intrinsic sense of touch we proposed in this work can serve as the basis for an advanced category of physical human-robot interaction that has not been possible yet, enabling a shift from conventional modalities toward adaptability, flexibility, and intuitive handling. Editor's summary: Tactile sensors and electronic skins are common devices that provide robots with a sense of physical interaction, but they become complex and costly when used to cover large portions of a robot. Using internal high-resolution joint-force-torque sensors, Iskandar et al. realized an intrinsic, full-body sense of touch in a robotic arm. Deep learning techniques and an artificial neural network allow the robot to sense the location, direction, and magnitude of a force applied anywhere on its surface. The robot can recognize and react to characters, such as a number drawn on its surface, and virtual buttons or sliders, providing users with an intuitive way to interact with a robot. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Autonomous robots operating in uncertain or hazardous environments subject to state safety constraints must be able to identify and isolate faulty components in a time-optimal manner. When the underlying fault is ambiguous and intertwined with the robot's state estimation, motion plans that discriminate between simultaneous actuator and sensor faults are necessary. However, the coupled fault mode and physical state uncertainty creates a constrained optimization problem that is challenging to solve with existing methods. We combined belief-space tree search, marginalized filtering, and concentration inequalities in our method, safe fault estimation via active sensing tree search (s-FEAST), a planner that actively diagnoses system faults by selecting actions that give the most informative observations while simultaneously enforcing probabilistic state constraints. We justify this approach with theoretical analysis showing s-FEAST's convergence to optimal policies. Using our robotic spacecraft simulator, we experimentally validated s-FEAST by safely and successfully performing fault estimation while on a collision course with a model comet. These results were further validated through extensive numerical simulations demonstrating s-FEAST's performance. Editor's summary: As autonomous systems are increasingly deployed in various real-world scenarios, the ability to safely accomplish their desired tasks is dependent on the ability to detect system failures and subsequently take the appropriate action without human intervention. Ragan et al. have developed a method—safe fault estimation via active sensing tree search—for diagnosing system faults capable of planning and acting to ensure safe robot operations. They demonstrated that the method could actively perform fault estimation on a robotic spacecraft simulator on a collision course with a model comet when thrusters are dysfunctional and showed its potential to keep the robot on a safe path. —Amos Matsiko [ABSTRACT FROM AUTHOR]

In contrast with jumping robots made from rigid materials, soft jumpers composed of compliant and elastically deformable materials exhibit superior impact resistance and mechanically robust functionality. However, recent efforts to create stimuli-responsive jumpers from soft materials were limited in their response speed, takeoff velocity, and travel distance. Here, we report a magnetic-driven, ultrafast bistable soft jumper that exhibits good jumping capability (jumping more than 108 body heights with a takeoff velocity of more than 2 meters per second) and fast response time (less than 15 milliseconds) compared with previous soft jumping robots. The snap-through transitions between bistable states form a repeatable loop that harnesses the ultrafast release of stored elastic energy. On the basis of the dynamic analysis, the multimodal locomotion of the bistable soft jumper can be realized: the interwell mode of jumping and the intrawell mode of hopping. These modes are controlled by adjusting the duration and strength of the magnetic field, which endows the bistable soft jumper with robust locomotion capabilities. In addition, it is capable of jumping omnidirectionally with tunable heights and distances. To demonstrate its capability in complex environments, a realistic pipeline with amphibious terrain was established. The jumper successfully finished a simulative task of cleansing water through a pipeline. The design principle and actuating mechanism of the bistable soft jumper can be further extended for other flexible systems. Editor's summary: For jumping to be an efficient mode of locomotion, it requires a high power output and fast response time, which can be difficult to achieve in soft materials. Here, Tang et al. developed a pyramid-shaped soft jumper with a fast snap-through transition triggered by a magnetic field. Made of magnetic panels and elastomer creases, the robot rapidly transitions from the top of the pyramid pointing up to pointing down, propelling the robot upward to more than 100 times its height. By adjusting the strength, duration, and direction of the magnetic field, the robot's movement can go from jumping to hopping, and its trajectory can be controlled, allowing it to navigate through confined spaces such as a pipeline. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Using artificial intelligence to facilitate autonomy in robotics will provide new insights into marine life. [ABSTRACT FROM AUTHOR]

Because minimally invasive surgery is increasingly used to target small lesions, demand is growing for miniaturized tools—such as microcatheters, articulated microforceps, or tweezers—that incorporate sensing and actuation for precision surgery. Although existing microfabrication techniques have addressed the construction of these devices, accurate integration and functionalization of chemical and physical sensors represent major challenges. This paper presents a microrobotic platform for the functionalization of fibers of diameters from 140 to 830 micrometers, with a patterning precision of 5 micrometers and an orientation error below 0.4°. To achieve this, we developed two 2 millimeter–by–3 millimeter, 200-micrometer-thick microrobots to align floating electronic circuits on a fiber during a wet transfer process. The position and orientation of the microrobots were controlled at the air/water interface by a permanent magnet. The stiffness of the position controlled was 0.2 newton millimeter, leading to an average force of 0.5 newton. The nonhomogeneous magnetic field of the magnet, associated with different preferred magnetization directions recorded in the microrobots, allowed the distance between the two microrobots to be precisely controlled. This extra degree of freedom was used to control the microrobot pair as a tweezer to grab and release floating electronic patterns, whereas the others were used to align the pattern position and orientation with the fiber. A model of this control, as well as the microrobots' interaction through surface tension, is proposed. Detailed performance validation is provided, and various exemplar sensor embodiments on a 200-micrometer-diameter fiber and three-dimensional devices are demonstrated. [ABSTRACT FROM AUTHOR]

Robotic vehicles that are capable of autonomously transitioning between various terrains and fluids have received notable attention in the past decade due to their potential to navigate previously unexplored and/or unpredictable environments. Specifically, aerial-aquatic mobility will enable robots to operate in cluttered aquatic environments and carry out a variety of sensing tasks. One of the principal challenges in the development of such vehicles is that the transition from water to flight is a power-intensive process. At a small scale, this is made more difficult by the limitations of electromechanical actuation and the unfavorable scaling of the physics involved. This paper investigates the use of solid reactants as a combustion gas source for consecutive aquatic jump-gliding sequences. We present an untethered robot that is capable of multiple launches from the water surface and of transitioning from jetting to a glide. The power required for aquatic jump-gliding is obtained by reacting calcium carbide powder with the available environmental water to produce combustible acetylene gas, allowing the robot to rapidly reach flight speed from water. The 160-gram robot could achieve a flight distance of 26 meters using 0.2 gram of calcium carbide. Here, the combustion process, jetting phase, and glide were modeled numerically and compared with experimental results. Combustion pressure and inertial measurements were collected on board during flight, and the vehicle trajectory and speed were analyzed using external tracking data. The proposed propulsion approach offers a promising solution for future high-power density aerial-aquatic propulsion in robotics. [ABSTRACT FROM AUTHOR]

Prosthetic hands are prescribed to patients who have suffered an amputation of the upper limb due to an accident or a disease. This is done to allow patients to regain functionality of their lost hands. Myoelectric prosthetic hands were found to have the possibility of implementing intuitive controls based on operator's electromyogram (EMG) signals. These controls have been extensively studied and developed. In recent years, development costs and maintainability of prosthetic hands have been improved through three-dimensional (3D) printing technology. However, no previous studies have realized the advantages of EMG-based classification of multiple finger movements in conjunction with the introduction of advanced control mechanisms based on human motion. This paper proposes a 3D-printed myoelectric prosthetic hand and an accompanying control system. The muscle synergy–based motion-determination method and biomimetic impedance control are introduced in the proposed system, enabling the classification of unlearned combined motions and smooth and intuitive finger movements of the prosthetic hand. We evaluate the proposed system through operational experiments performed on six healthy participants and an upper-limb amputee participant. The experimental results demonstrate that our prosthetic hand system can successfully classify both learned single motions and unlearned combined motions from EMG signals with a high degree of accuracy. Furthermore, applications to real-world uses of prosthetic hands are demonstrated through control tasks conducted by the amputee participant. [ABSTRACT FROM AUTHOR]

This paper applies information theoretic principles to the investigation of physical human-robot interaction. Drawing from the study of human perception and neural encoding, information theoretic approaches offer a perspective that enables quantitatively interpreting the body as an information channel and bodily motion as an information-carrying signal. We show that ergodicity, which can be interpreted as the degree to which a trajectory encodes information about a task, correctly predicts changes due to reduction of a person's existing deficit or the addition of algorithmic assistance. The measure also captures changes from training with robotic assistance. Other common measures for assessment failed to capture at least one of these effects. This information-based interpretation of motion can be applied broadly, in the evaluation and design of human-machine interactions, in learning by demonstration paradigms, or in human motion analysis. [ABSTRACT FROM AUTHOR]

Universal picking (UP), or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challenge for e-commerce order fulfillment, manufacturing, inspection, and home service robots. Optimizing the rate, reliability, and range of UP is difficult due to inherent uncertainty in sensing, control, and contact physics. This paper explores "ambidextrous" robot grasping, where two or more heterogeneous grippers are used. We present Dexterity Network (Dex-Net) 4.0, a substantial extension to previous versions of Dex-Net that learns policies for a given set of grippers by training on synthetic datasets using domain randomization with analytic models of physics and geometry. We train policies for a parallel-jaw and a vacuum-based suction cup gripper on 5 million synthetic depth images, grasps, and rewards generated from heaps of three-dimensional objects. On a physical robot with two grippers, the Dex-Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of more than 300 mean picks per hour. [ABSTRACT FROM AUTHOR]

In a step toward soft robot proprioception, and therefore better control, this paper presents an internally illuminated elastomer foam that has been trained to detect its own deformation through machine learning techniques. Optical fibers transmitted light into the foam and simultaneously received diffuse waves from internal reflection. The diffuse reflected light was interpreted by machine learning techniques to predict whether the foam was twisted clockwise, twisted counterclockwise, bent up, or bent down. Machine learning techniques were also used to predict the magnitude of the deformation type. On new data points, the model predicted the type of deformation with 100%accuracy and the magnitude of the deformation with a mean absolute error of 0.06°. This capability may impart soft robots with more complete proprioception, enabling them to be reliably controlled and responsive to external stimuli. [ABSTRACT FROM AUTHOR]

Microhydraulic actuators offer a new way to convert electrical power to mechanical power on a microscale with an unmatched combination of power density and efficiency. Actuators work by combining surface tension force contributions from a large number of droplets distorted by electrowetting electrodes. This paper reports on the behavior of microgram-scale linear and rotational microhydraulic actuators with output force/weight ratios of 5500, cycle frequencies of 4 kilohertz, <1-micrometer movement precision, and accelerations of 3 kilometers/second2. The power density and the efficiency of the actuators were characterized by simultaneously measuring the mechanical work performed and the electrical power applied. Maximum output power density was 0.93 kilowatt/kilogram, comparable with the best electric motors. At maximum power, the actuator was 60% efficient, but efficiencies were as high as 83% at lower power. Rotational actuators demonstrated a torque density of 79 newton meters/kilogram, substantially more than electric motors of comparable diameter. Scaling the droplet pitch from 100 to 48 micrometers increased power density from 0.27 to 0.93 kilowatt/kilogram, validating the quadratic scaling of actuator power. [ABSTRACT FROM AUTHOR]

We address a fundamental issue of collective motion of aerial robots: how to ensure that large flocks of autonomous drones seamlessly navigate in confined spaces. The numerous existing flocking models are rarely tested on actual hardware because they typically neglect some crucial aspects of multirobot systems. Constrained motion and communication capabilities, delays, perturbations, or the presence of barriers should be modeled and treated explicitly because they have large effects on collective behavior during the cooperation of real agents. Handling these issues properly results in additional model complexity and a natural increase in the number of tunable parameters, which calls for appropriate optimization methods to be coupled tightly to model development. In this paper, we propose such a flocking model for real drones incorporating an evolutionary optimization framework with carefully chosen order parameters and fitness functions. We numerically demonstrated that the induced swarm behavior remained stable under realistic conditions for large flock sizes and notably for large velocities. We showed that coherent and realistic collective motion patterns persisted even around perturbing obstacles. Furthermore, we validated our model on real hardware, carrying out field experiments with a self-organized swarm of 30 drones. This is the largest of such aerial outdoor systems without central control reported to date exhibiting flocking with collective collision and object avoidance. The results confirmed the adequacy of our approach. Successfully controlling dozens of quadcopters will enable substantially more efficient task management in various contexts involving drones. [ABSTRACT FROM AUTHOR]

The precise delivery of targeted cells through magnetic field–driven microrobots/carriers is a promising technique for targeted therapy and tissue regeneration. This paper presents a microrobot designed with a burr-like porous spherical structure for carrying and delivering targeted cells in vivo under a magnetic gradient field–driven mechanism. The robot was fabricated by using three-dimensional laser lithography and coated with Ni for magnetic actuation and Ti for biocompatibility. Numerical and experimental studies demonstrated that the proposed microrobot design could enhance magnetic driving capability, promote cell-carrying capacity, and benefit cell viability. Microrobots loaded with cells could be automatically controlled to reach a desired site by using a self-constructed electromagnetic coil system, as verified by in vivo transport of cell-cultured microrobots in zebrafish embryos. The carried cells could be spontaneously released from the microrobot to the surrounding tissues; in vitro experiments showed that cells from the microrobot were directly released onto the desired site or were able to pass through the blood vessel–like microchannel to arrive at the delivery area. Further in vivo cell-releasing tests were performed on nude mice, followed by histological study. This research provides a microrobotic device platform for regenerative medicine and cell-based therapy. [ABSTRACT FROM AUTHOR]

Micro/nanomachines capable of propulsion through fluidic environments provide diverse opportunities in important biomedical applications. In this paper, we present a theoretical study on micromotors steered through liquid by an external rotating magnetic field. A purely geometric tight upper bound on the propulsion speed normalized with field frequency, known as propulsion efficiency, δ, for an arbitrarily shaped object is derived. Using this bound, we estimate the maximum propulsion efficiency of previously reported random magnetic aggregates. We introduce a complementary definition of the propulsion efficiency, δ*, that ranks propellers according to their maximal speed in body lengths per unit time and that appears to be preferable over the standard definition in a search for fastest machines. Using a bead-based hydrodynamic model combined with genetic algorithms, we determine that δ*-optimal propeller deviates strongly from the bioinspired slim helix and has a surprising chubby skew-symmetric shape. It is also shown that optimized propellers with preprogrammed shape are substantially more efficient than random magnetic aggregates. We anticipate that the results of the present study will provide guidance toward prospective experimental design of more efficient magnetic micro/nanomachines. [ABSTRACT FROM AUTHOR]

Over the past 50 years, there has been a marked increase in diseases like dengue fever, chikungunya, and Zika. The World Mosquito Program (WMP) has developed an approach that, instead of attempting to eliminate vector species, introduces Wolbachia into native Aedes aegypti populations through the release of Wolbachia-infected mosquitoes. Using this approach, a randomized controlled study recently demonstrated a 77% reduction in dengue across a treatment area within Yogyakarta, Indonesia. Existing release methods use the ground-based release of mosquito eggs or adults that are labor-intensive, are logistically challenging to scale up, and can be restrictive in areas where staff safety is a concern. To overcome these limitations, we developed a fully automated mosquito dosing release system that released smaller cohorts of mosquitoes over a wide area and integrated it into an uncrewed aerial vehicle. We established the effectiveness of this system using an aerial mark, release, and recapture approach. We then demonstrated that using only the aerial release method, we can establish Wolbachia infection in a naive Ae. aegypti population. In both cases, the use of aerial releases demonstrated comparable outcomes to ground-based releases without the required labor or risk. These two trials demonstrated the feasibility of using an aerial release approach for large-scale mosquito releases. Editor's summary: The World Mosquito Program recently developed a technique to control dengue transmission by releasing Wolbachia-infected Aedes aegypti mosquitoes. Wolbachia bacteria act as a virus-blocking agent and are maternally transmitted to the local mosquito populations. However, scaling ground-based releases to large geographic areas poses a challenge. To address this, Lin et al. developed an automated mosquito dosing release system and incorporated it into an uncrewed aerial vehicle (UAV). This system, which includes temperature and humidity control, is designed to release 150 mosquitos per dose. Two successful field trials in Fiji demonstrated that the UAV-based release achieved similar uniformity to ground release methods and successfully established Wolbachia infections in the native population over a 2-km2 area. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Effective mosquito population suppression has been repeatedly demonstrated in field trials through the release of male mosquitoes to induce sterile mating with wild females using the incompatible insect technique (IIT), the sterile insect technique (SIT), or their combination. However, upscaling these techniques requires a highly efficient and scalable approach for the sex separation of mass-reared mosquitoes to minimize the unintentional release of females, which can lead to either population replacement or biting nuisance, a major bottleneck up to now. Here, we report the successful development of an automated mosquito pupa sex sorter that can effectively separate large numbers of males from females for population suppression of Aedes aegypti, A. albopictus, and Culex quinquefasciatus. The male production capacity of the automated sex sorter was increased by ~17-fold compared with manual sex separation with the Fay-Morlan sorter and enabled one person to separate 16 million males per week. With ~0.5% female contamination, the produced males exhibited high flight ability and mating performance. The field trial demonstrates that the quality of A. albopictus males produced using the automated sex sorter is suitable for inducing population suppression. These results indicate that the automated sex sorter offers the potential to upscale IIT and SIT against mosquito vectors for disease control. Editor's summary: Several techniques have been developed to fight mosquito-borne diseases, including the release of sterile or incompatible male mosquitoes into the wild to reduce population growth. However, sorting mosquitoes by sex is a laborious process. Gong et al. have now developed an automated mosquito pupa sex sorter, which was tested on three mosquito species and shown to increase the production of males 17-fold when compared with a manual separation process. A field trial in Guangzhou, China showed that the process was capable of producing sufficient incompatible males without reducing their quality, which led to the suppression of wild populations of mosquitoes. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Resilience is crucial for the self-preservation of biological systems: Humans recover from wounds thanks to an immune system that autonomously enacts a multistage response to promote healing. Similar passive mechanisms can enable pneumatic soft robots to overcome common faults such as bursts originating from punctures or overpressurization. Recent technological advancements, ranging from fault-tolerant controllers for robot reconfigurability to self-healing materials, have paved the way for robot resilience. However, these techniques require powerful processors and large datasets or external hardware. How to extend the operational life span of damaged soft robots with minimal computational and physical resources remains unclear. In this study, we demonstrated a multimodal pneumatic soft valve capable of passive resilient reactions, triggered by faults, to prevent or isolate damage in soft robots. In its forward operation mode, the valve, requiring a single supply pressure, isolated punctured soft inflatable elements from the rest of the soft robot in as fast as 21 milliseconds. In its reverse operation mode, the valve can passively protect robots against overpressurization caused by external disturbances, avoiding plastic deformations and bursts. Furthermore, the two modes combined enabled the creation of an endogenously controlled valve capable of autonomous burst isolation. We demonstrated the passive and quick response and the possibility of monolithic integration of the soft valve in grippers and crawling robots. The approach proposed in this study provides a distributed small-footprint alternative to controller-based resilience and is expected to help soft robots achieve uninterrupted long-lasting operation. Editor's summary: Fluidic soft robots are vulnerable to failure from overpressurization and damage that leads to sudden loss of pressurization. To give these robots more resilience and extend their lifetimes, Pontin and Damian developed a multimodal pneumatic soft valve designed to isolate damage and avoid catastrophic failure. The valve passively reacts to pressure differences triggered by faults. In the forward operating mode, the valve can detect a burst and autonomously isolate the damaged section, maintaining pressure in the other parts of the robot. In the reverse operating mode, the valve can detect overpressurization and release air to prevent irreversible damage. The valve capabilities were demonstrated in a five-finger soft gripper, a two-finger soft hand, and a soft crawler that remained operational after damage. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Navigation is an essential capability for autonomous robots. In particular, visual navigation has been a major research topic in robotics because cameras are lightweight, power-efficient sensors that provide rich information on the environment. However, the main challenge of visual navigation is that it requires substantial computational power and memory for visual processing and storage of the results. As of yet, this has precluded its use on small, extremely resource-constrained robots such as lightweight drones. Inspired by the parsimony of natural intelligence, we propose an insect-inspired approach toward visual navigation that is specifically aimed at extremely resource-restricted robots. It is a route-following approach in which a robot's outbound trajectory is stored as a collection of highly compressed panoramic images together with their spatial relationships as measured with odometry. During the inbound journey, the robot uses a combination of odometry and visual homing to return to the stored locations, with visual homing preventing the buildup of odometric drift. A main advancement of the proposed strategy is that the number of stored compressed images is minimized by spacing them apart as far as the accuracy of odometry allows. To demonstrate the suitability for small systems, we implemented the strategy on a tiny 56-gram drone. The drone could successfully follow routes up to 100 meters with a trajectory representation that consumed less than 20 bytes per meter. The presented method forms a substantial step toward the autonomous visual navigation of tiny robots, facilitating their more widespread application. Editor's summary: For navigation, robots often rely either on external infrastructure or on mapping-based navigation algorithms that are generally associated with high computational demands. van Dijk et al. have now developed an insect-inspired approach for visual navigation of resource-constrained miniature drones by exploiting both visual homing and odometry. The framework was tested on a 56-g drone, which was shown to be capable of following routes in indoor environments over long distances with minimal memory requirements. This approach could substantially improve the autonomous navigation of aerial robots with constraints in computational power, energy demands, and weight. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Cytokines have been identified as key contributors to the development of inflammatory bowel disease (IBD), yet conventional treatments often prove inadequate and carry substantial side effects. Here, we present an innovative biohybrid robotic system, termed "algae-MΦNP-robot," for addressing IBD by actively neutralizing colonic cytokine levels. Our approach combines moving green microalgae with macrophage membrane–coated nanoparticles (MΦNPs) to efficiently capture proinflammatory cytokines "on the fly." The dynamic algae-MΦNP-robots outperformed static counterparts by enhancing cytokine removal through continuous movement, better distribution, and extended retention in the colon. This system is encapsulated in an oral capsule, which shields it from gastric acidity and ensures functionality upon reaching the targeted disease site. The resulting algae-MΦNP-robot capsule effectively regulated cytokine levels, facilitating the healing of damaged epithelial barriers. It showed markedly improved prevention and treatment efficacy in a mouse model of IBD and demonstrated an excellent biosafety profile. Overall, our biohybrid algae-MΦNP-robot system offers a promising and efficient solution for IBD, addressing cytokine-related inflammation effectively. Editor's summary: Regulating cytokine activity can effectively manage inflammatory bowel disease, but traditional treatments are immunosuppressant or only target specific cytokines. By combining the robust motion of green algae with the binding abilities of macrophage membrane–coated nanoparticles, Li et al. developed a biohybrid microrobot that captured and removed a range of proinflammatory cytokines in vitro. The algae-based robots were encapsulated for protective passage through the stomach and administered to mice. In vivo studies confirmed biosafety and successful prevention and treatment of inflammatory bowel disease in a mouse model. The algae-based robots also hold potential for treatment of other cytokine-related disorders. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Our understanding of cardiac remodeling processes due to left ventricular pressure overload derives largely from animal models of aortic banding. However, these studies fail to enable control over both disease progression and reversal, hindering their clinical relevance. Here, we describe a method for progressive and reversible aortic banding based on an implantable expandable actuator that can be finely tuned to modulate aortic banding and debanding in a rat model. Through catheterization, imaging, and histologic studies, we demonstrate that our platform can recapitulate the hemodynamic and structural changes associated with pressure overload in a controllable manner. We leveraged soft robotics to enable noninvasive aortic debanding, demonstrating that these changes can be partly reversed because of cessation of the biomechanical stimulus. By recapitulating longitudinal disease progression and reversibility, this animal model could elucidate fundamental mechanisms of cardiac remodeling and optimize timing of intervention for pressure overload. Editor's summary: Understanding the processes involved in heart failure due to cardiac diseases is crucial in the development of effective interventions. However, tunable physical models that can suitably replicate these processes progressively are lacking. Rosalia et al. have now used implantable soft robot actuators to develop a tunable platform that can model progressive and reversible aortic constriction in small animals. With the aid of magnetic resonance imaging, they demonstrated the hemodynamic changes due to pressure overload from aortic banding and debanding, replicating the progression and reversal of cardiac disease. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Repetitive overhead tasks during factory work can cause shoulder injuries resulting in impaired health and productivity loss. Soft wearable upper extremity robots have the potential to be effective injury prevention tools with minimal restrictions using soft materials and active controls. We present the design and evaluation of a portable inflatable shoulder wearable robot for assisting industrial workers during shoulder-elevated tasks. The robot is worn like a shirt with integrated textile pneumatic actuators, inertial measurement units, and a portable actuation unit. It can provide up to 6.6 newton-meters of torque to support the shoulder and cycle assistance on and off at six times per minute. From human participant evaluations during simulated industrial tasks, the robot reduced agonist muscle activities (anterior, middle, and posterior deltoids and biceps brachii) by up to 40% with slight changes in joint angles of less than 7% range of motion while not increasing antagonistic muscle activity (latissimus dorsi) in current sample size. Comparison of controller parameters further highlighted that higher assistance magnitude and earlier assistance timing resulted in statistically significant muscle activity reductions. During a task circuit with dynamic transitions among the tasks, the kinematics-based controller of the robot showed robustness to misinflations (96% true negative rate and 91% true positive rate), indicating minimal disturbances to the user when assistance was not required. A preliminary evaluation of a pressure modulation profile also highlighted a trade-off between user perception and hardware demands. Finally, five automotive factory workers used the robot in a pilot manufacturing area and provided feedback. Editor's summary: Labor-intensive industrial work involving repetitive lifting of arms has been associated with several shoulder injuries. Significant progress has been made in the development of passive exoskeletons that typically use springs to provide assistance, but the assistance level is not easily adaptable to different user motions. Zhou et al. have now developed a portable soft wearable robot to actively assist the shoulder in industrial use cases, showing the potential to reduce muscle activity during drilling and holding tasks in a laboratory setting. Use of the device by workers in an automotive industrial setting was also demonstrated. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Sense of joint agency (SoJA) is the sense of control experienced by humans when acting with others to bring about changes in the shared environment. SoJA is proposed to arise from the sensorimotor predictive processes underlying action control and monitoring. Because SoJA is a ubiquitous phenomenon occurring when we perform actions with other humans, it is of great interest and importance to understand whether—and under what conditions—SoJA occurs in collaborative tasks with humanoid robots. In this study, using behavioral measures and neural responses measured by electroencephalography (EEG), we aimed to evaluate whether SoJA occurs in joint action with the humanoid robot iCub and whether its emergence is influenced by the perceived intentionality of the robot. Behavioral results show that participants experienced SoJA with the robot partner when it was presented as an intentional agent but not when it was presented as a mechanical artifact. EEG results show that the mechanism that influences the emergence of SoJA in the condition when the robot is presented as an intentional agent is the ability to form similarly accurate predictions about the sensory consequences of our own and others' actions, leading to similar modulatory activity over sensory processing. Together, our results shed light on the joint sensorimotor processing mechanisms underlying the emergence of SoJA in human-robot interaction and underscore the importance of attribution of intentionality to the robot in human-robot collaboration. Editor's summary: The sense of joint agency can emerge when humans collaborate to complete tasks. They experience a perception or feeling of shared control over joint actions and their outcomes. Navare et al. investigated whether the sense of joint agency can occur between humans and humanoid robots during collaborative tasks. They used neural and behavioral measures to show that human participants experienced this sense of joint agency, underpinned by mechanisms of joint sensorimotor processing, when a robot was presented as an intentional agent. However, a sense of joint agency was not felt when the robot was presented as a mechanical artifact. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Robotic spacecraft enable exploration of our Solar System beyond our human presence. Although spacecraft have explored every planet in the Solar System, the frontiers of space robotics are at the cutting edge of landers, rovers, and now atmospheric explorers, where robotic spacecraft must interact intimately with their environment to explore beyond the reach of flyby and orbital remote sensing. Here, we describe the tremendous growth in space robotics missions in the past 7 years, with many new entities participating in missions to the surface of the Moon, Mars, and beyond. We also describe the recent development of aerial missions to planets and moons, as exemplified by the Ingenuity helicopter on Mars and the Dragonfly mission to Titan. We focus on suborbital robotics—landers, rovers, and aerial vehicles—with associated challenges in sensing, manipulation, mobility, and system-level autonomy. [ABSTRACT FROM AUTHOR]

This Review discusses the main results obtained in training end-to-end neural architectures for guidance and control of interplanetary transfers, planetary landings, and close-proximity operations, highlighting the successful learning of optimality principles by the underlying neural models. Spacecraft and drones aimed at exploring our solar system are designed to operate in conditions where the smart use of onboard resources is vital to the success or failure of the mission. Sensorimotor actions are thus often derived from high-level, quantifiable, optimality principles assigned to each task, using consolidated tools in optimal control theory. The planned actions are derived on the ground and transferred on board, where controllers have the task of tracking the uploaded guidance profile. Here, we review recent trends based on the use of end-to-end networks, called guidance and control networks (G&CNets), which allow spacecraft to depart from such an architecture and to embrace the onboard computation of optimal actions. In this way, the sensor information is transformed in real time into optimal plans, thus increasing mission autonomy and robustness. We then analyze drone racing as an ideal gym environment to test these architectures on real robotic platforms and thus increase confidence in their use in future space exploration missions. Drone racing not only shares with spacecraft missions both limited onboard computational capabilities and similar control structures induced from the optimality principle sought but also entails different levels of uncertainties and unmodeled effects and a very different dynamical timescale. [ABSTRACT FROM AUTHOR]

Closed-loop neuroprostheses show promise in restoring motion in individuals with neurological conditions. However, conventional activation strategies based on functional electrical stimulation (FES) fail to accurately modulate muscle force and exhibit rapid fatigue because of their unphysiological recruitment mechanism. Here, we present a closed-loop control framework that leverages physiological force modulation under functional optogenetic stimulation (FOS) to enable high-fidelity muscle control for extended periods of time (>60 minutes) in vivo. We first uncovered the force modulation characteristic of FOS, showing more physiological recruitment and significantly higher modulation ranges (>320%) compared with FES. Second, we developed a neuromuscular model that accurately describes the highly nonlinear dynamics of optogenetically stimulated muscle. Third, on the basis of the optogenetic model, we demonstrated real-time control of muscle force with improved performance and fatigue resistance compared with FES. This work lays the foundation for fatigue-resistant neuroprostheses and optogenetically controlled biohybrid robots with high-fidelity force modulation. Editor's summary: Electrical stimulation has been widely used in neuroprostheses to control and restore muscle function. However, this approach causes poor fine-force modulation and quickly leads to fatiguing of the muscles after brief stimulation. Herrera-Arcos et al. now present a closed-loop method for neuromodulation using optogenetics that alleviates the challenges associated with electrical stimulation. A biophysical model was also developed to understand the characteristics underlying neuromuscular optogenetic stimulation. They showed that functional optogenetic stimulation supported fatigue-resistant control of muscle with higher accuracy and higher generated force in vivo than electrical stimulation, demonstrating that it may potentially be adopted for prosthesis control. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Agility and trajectory control are two desirable features for robotics, but they become very challenging for soft robots without rigid structures to support rapid manipulations. Here, a curved piezoelectric thin film driven at its structural resonant frequency is used as the main body of an insect-scale soft robot for its fast translational movements, and two electrostatic footpads are used for its swift rotational motions. These two schemes are simultaneously executed during operations through a simple two-wire connection arrangement. A high relative centripetal acceleration of 28 body length per square second compared with existing robots is realized on a 65-milligram tethered prototype, which is better than those of common insects, including the cockroach. The trajectory manipulation demonstration is accomplished by navigating the robot to pass through a 120-centimeter-long track in a maze within 5.6 seconds. One potential application is presented by carrying a 180-milligram on-board sensor to record a gas concentration route map and to identify the location of the leakage source. The radically simplified analog motion adjustment technique enables the scale-up construction of a 240-milligram untethered robot. Equipped with a payload of 1660 milligrams to include the control circuit, a battery, and photoresistors, the untethered prototype can follow a designated, 27.9-centimeter-long "S"-shaped path in 36.9 seconds. These results validate key performance attributes in achieving both high mobility and agility to emulate living agile insects for the advancements of soft robots. [ABSTRACT FROM AUTHOR]

Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that uses extendable booms as appendages to manipulate itself with respect to irregular rock surfaces. The booms terminate in grippers equipped with microspines and provide ReachBot with a large workspace, allowing it to achieve force closure in enclosed spaces, such as the walls of a lava tube. To propel ReachBot, we present a contact-before-motion planner for nongaited legged locomotion that uses internal force control, similar to a multifingered hand, to keep its long, slender booms in tension. Motion planning also depends on finding and executing secure grips on rock features. We used a Monte Carlo simulation to inform gripper design and predict grasp strength and variability. In addition, we used a two-step perception system to identify possible grasp locations. To validate our approach and mechanisms under realistic conditions, we deployed a single ReachBot arm and gripper in a lava tube in the Mojave Desert. The field test confirmed that ReachBot will find many targets for secure grasps with the proposed kinematic design. Editor's summary: Caves on the moon and Mars are of geological and astrobiological interest, but their rocky terrain and sparse anchor points make it impossible for traditional robots to explore. Taking inspiration from an arachnid, Chen et al. developed a robot with a small body and long extendable booms for appendages named ReachBot. The ends of the booms were equipped with grippers that can grasp rocky terrain and enable ReachBot to use manipulation for locomotion. Using onboard sensors along with a contact-before-motion planner, the robot could scan and identify graspable convex features. Field tests performed in a lava tube in the Mojave Desert (California, USA) confirmed that ReachBot could identify and grasp multiple sites, demonstrating its potential as a martian explorer. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we developed a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond. Editor's summary: Last-mile deliveries in urban areas lead to increasing traffic problems and supply chain issues. By converting to robotic deliveries, routes can move from the street to alternate pathways, but traditional legged robots lack the necessary efficiency and battery life. By augmenting a legged robot with wheels, Lee et al. used hybrid wheeled-legged locomotion to improve the cost of transport while rolling on flat surfaces yet still allowing for legged navigation over obstacles like stairs. Using various reinforcement learning techniques, the robot was trained to smoothly transition between walking and driving modes and to navigate challenging terrain and dynamic obstacles including pedestrians. The robot was tested for large-scale navigation through an autonomous trek of more than 10 kilometers in Zurich, Switzerland, and Seville, Spain. The hybrid wheeled-legged platform successfully addressed many challenges associated with robotic urban mobility. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Nature abounds with examples of superior mobility through the fusion of aerial and ground movement. Drawing inspiration from such multimodal locomotion, we introduce a high-performance hybrid hopping and flying robot. The proposed robot seamlessly integrates a nano quadcopter with a passive telescopic leg, overcoming limitations of previous jumping mechanisms that rely on stance phase leg actuation. Based on the identified dynamics, a thrust-based control method and detachable active aerodynamic surfaces were devised for the robot to perform continuous jumps with and without position feedback. This unique design and actuation strategy enable tuning of jump height and reduced stance phase duration, leading to agile hopping locomotion. The robot recorded an average vertical hopping speed of 2.38 meters per second at a jump height of 1.63 meters. By harnessing multimodal locomotion, the robot is capable of intermittent midflight jumps that result in substantial instantaneous accelerations and rapid changes in flight direction, offering enhanced agility and versatility in complex environments. The passive leg design holds potential for direct integration with conventional rotorcraft, unlocking seamless hybrid hopping and flying locomotion. Editor's summary: Conventional jumping robots are actuated in a stance phase, either by a rapid energy release mechanism or with legs driven by actuators. Previous attempts at a jumping and flying robot used these mechanisms for jump-assisted takeoff but struggled to achieve continuous hopping or adjustable jump heights. A new robot called the Hopcopter by Bai et al. combines a commercial quadcopter with a passive telescopic leg. The Hopcopter was actuated in the aerial phase, allowing for continuous hopping, high jump frequencies, and tunable jump height. Intermittent hops led to sudden increases in acceleration and enable highly agile movements such as rapid, tight turns. The robot was also modified with an aerodynamic stabilizer that interacts with air flow to stabilize the robot with only internal sensors. The combination of locomotion modes was shown to increase endurance and power efficiency, which could potentially extend travel distance and operational range for applications in surveillance and inspection. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

Humanoid robots that can autonomously operate in diverse environments have the potential to help address labor shortages in factories, assist elderly at home, and colonize new planets. Although classical controllers for humanoid robots have shown impressive results in a number of settings, they are challenging to generalize and adapt to new environments. Here, we present a fully learning-based approach for real-world humanoid locomotion. Our controller is a causal transformer that takes the history of proprioceptive observations and actions as input and predicts the next action. We hypothesized that the observation-action history contains useful information about the world that a powerful transformer model can use to adapt its behavior in context, without updating its weights. We trained our model with large-scale model-free reinforcement learning on an ensemble of randomized environments in simulation and deployed it to the real-world zero-shot. Our controller could walk over various outdoor terrains, was robust to external disturbances, and could adapt in context. Editor's summary: The ability of robots to navigate adaptively and robustly in varying terrain increases their chances of success when deployed in the real world. However, stable locomotion of full-size bipedal humanoid robots creates a challenge from a controls perspective. Radosavovic et al. developed a reinforcement learning approach for controlling locomotion of a humanoid robot, Digit. They trained their model in simulation and subsequently deployed it into the real-world zero-shot and showed the potential for robust locomotion on various indoor and outdoor environments. The robot could exhibit natural and adaptive walking behaviors, including an emergent arm-swing motion, and adapt to external perturbations. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Electric motors are widely used in robots but waste energy in many applications. We introduce an elastic energy-recycling actuator that maintains the versatility of motors while improving energy efficiency in cyclic tasks. The actuator comprises a motor in parallel with an array of springs that can be individually engaged and disengaged, while retaining stored energy, by pairs of low-power electroadhesive clutches. We developed a prototype actuator and tested it in five repetitive tasks with features common in robotic applications but difficult to perform efficiently. The actuator reduced power consumption by at least 50% in all cases and by 97% in the best case. Elastic energy recovery, controlled by low-power clutches, can improve the efficiency of mobile robots, assistive devices, and other engineered systems. Editor's summary: Thermal loss in electric motors leads to a reduction in energy efficiency of the devices they power. Springs, on the other hand, can produce torque without consuming energy, but they are challenging to control. Krimsky and Collins have developed an actuator that combines the benefits of motors and springs, having both fine torque control and efficient energy recovery. A prototype actuator was tested in a series of cyclic test cases, demonstrating the potential to reduce energy consumption by 50 to 97% and highlighting the wide-reaching implications in the development of robotic systems with low energy consumption. —Amos Matsiko [ABSTRACT FROM AUTHOR]

Wireless millimeter-scale robots capable of navigating through fluid-flowing tubular structures hold substantial potential for inspection, maintenance, or repair use in nuclear, industrial, and medical applications. However, prevalent reliance on external powering constrains these robots' operational range and applicable environments. Alternatives with onboard powering must trade off size, functionality, and operation duration. Here, we propose a wireless millimeter-scale wheeled robot capable of using environmental flows to power and actuate its long-distance locomotion through complex pipelines. The flow-powering module can convert flow energy into mechanical energy, achieving an impeller speed of up to 9595 revolutions per minute, accompanied by an output power density of 11.7 watts per cubic meter and an efficiency of 33.7%. A miniature gearbox module can further transmit the converted mechanical energy into the robot's locomotion system, allowing the robot to move against water flow at an average rate of up to 1.05 meters per second. The robot's motion status (moving against/with flow or pausing) can be switched using an external magnetic field or an onboard mechanical regulator, contingent on different proposed control designs. In addition, we designed kirigami-based soft wheels for adaptive locomotion. The robot can move against flows of various substances within pipes featuring complex geometries and diverse materials. Solely powered by flow, the robot can transport cylindrical payloads with a diameter of up to 55% of the pipe's diameter and carry devices such as an endoscopic camera for pipeline inspection, a wireless temperature sensor for environmental temperature monitoring, and a leak-stopper shell for infrastructure maintenance. Editor's summary: Millimeter-scale robots that are capable of traversing through pipes for maintenance and inspection often rely on external power, but this dependence limits the robot's operational range and environment. Conversely, pipe inspection robots with onboard powering and actuation units cannot be scaled down to the millimeter scale. As an alternate power source, Hong et al. developed a miniature robot that relies on environmental power to traverse a pipeline. The wheeled robot, equipped with an internal impeller, converts the fluid flow in the tube into mechanical energy. An external magnetic field can switch the direction the impeller rotates, enabling movement both with and against direction of flow. Flexible wheels enable the robot to traverse complex pipe shapes with changing diameter. By using flow power within the pipe, this robot can move long distances and perform for long durations. —Melisa Yashinski [ABSTRACT FROM AUTHOR]