Fast-Response Variable-Stiffness Magnetic Catheters for Minimally Invasive Surgery.

In minimally invasive surgery, such as cardiac ablation, magnetically steered catheters made of variable-stiffness materials can enable higher dexterity and higher force application to human tissue. However, the long transition time between soft and rigid states leads to a significant increase in procedure duration. Here, a fast-response, multisegmented catheter is described for minimally invasive surgery made of variable-stiffness thread (FRVST) that encapsulates a helical cooling channel. The rapid stiffness change in the FRVST, composed of a nontoxic shape memory polymer, is achieved by an active cooling system that pumps water through the helical channel. The FRVST displays a 66 times stiffness change and a 26 times transition enhancement compare with the noncooled version. The catheter allows for selective bending of each segment up to 127° in air and up to 76° in water under an 80 mT external magnetic field. The inner working channel can be used for cooling an ablation tip during a procedure and for information exchange via the deployment of wires or surgical tools.

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research.

Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

The evolution of behavioral cues and signaling in displaced communication.

Displaced communication, whereby individuals communicate regarding a subject that is not immediately present (spatially or temporally), is one of the key features of human language. It also occurs in a few animal species, most notably the honeybee, where the waggle dance is used to communicate the location and quality of a patch of flowers. However, it is difficult to study how it emerged given the paucity of species displaying this capacity and the fact that it often occurs via complex multimodal signals. To address this issue, we developed a novel paradigm in which we conducted experimental evolution with foraging agents endowed with neural networks that regulate their movement and the production of signals. Displaced communication readily evolved but, surprisingly, agents did not use signal amplitude to convey information on food location. Instead, they used signal onset-delay and duration-based mode of communication, which depends on the motion of the agent within a communication area. When agents were experimentally prevented from using these modes of communication, they evolved to use signal amplitude instead. Interestingly, this mode of communication was more efficient and led to higher performance. Subsequent controlled experiments suggested that this more efficient mode of communication failed to evolve because it took more generations to emerge than communication grounded on the onset-delay and length of signaling. These results reveal that displaced communication is likely to initially evolve from non-communicative behavioral cues providing incidental information with evolution later leading to more efficient communication systems through a ritualization process.

What we look for at Science Robotics.

Science Robotics welcomes papers demonstrating technical and scientific advances, with potential for influence beyond robotics.

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones.

In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

How to compete with robots by assessing job automation risks and resilient alternatives.

The effects of robotics and artificial intelligence (AI) on the job market are matters of great social concern. Economists and technology experts are debating at what rate, and to what extent, technology could be used to replace humans in occupations, and what actions could mitigate the unemployment that would result. To this end, it is important to predict which jobs could be automated in the future and what workers could do to move to occupations at lower risk of automation. Here, we calculate the automation risk of almost 1000 existing occupations by quantitatively assessing to what extent robotics and AI abilities can replace human abilities required for those jobs. Furthermore, we introduce a method to find, for any occupation, alternatives that maximize the reduction in automation risk while minimizing the retraining effort. We apply the method to the U.S. workforce composition and show that it could substantially reduce the workers' automation risk, while the associated retraining effort would be moderate. Governments could use the proposed method to evaluate the unemployment risk of their populations and to adjust educational policies. Robotics companies could use it as a tool to better understand market needs, and members of the public could use it to identify the easiest route to reposition themselves on the job market.

Conditions for the emergence of circumnutations in plant roots.

The plant root system shows remarkably complex behaviors driven by environmental cues and internal dynamics, whose interplay remains largely unknown. A notable example is circumnutation growth movements, which are growth oscillations from side to side of the root apex. Here we describe a model capable of replicating root growth behaviors, which we used to analyze the role of circumnuntations, revealing their emergence I) under gravitropic stress, as a combination of signal propagation and sensitivity to the signal carriers; II) as a result of the interplay between gravitropic and thigmotropic responses; and III) as a behavioral strategy to detect and react to resource gradients. The latter function requires the presence of a hypothetical internal oscillator whose parameters are regulated by the perception of environmental resources.

Bioinspired wing and tail morphing extends drone flight capabilities.

The aerodynamic designs of winged drones are optimized for specific flight regimes. Large lifting surfaces provide maneuverability and agility but result in larger power consumption, and thus lower range, when flying fast compared with small lifting surfaces. Birds like the northern goshawk meet these opposing aerodynamic requirements of aggressive flight in dense forests and fast cruising in the open terrain by adapting wing and tail areas. Here, we show that this morphing strategy and the synergy of the two morphing surfaces can notably improve the agility, maneuverability, stability, flight speed range, and required power of a drone in different flight regimes by means of an avian-inspired drone. We characterize the drone's flight capabilities for different morphing configurations in wind tunnel tests, optimization studies, and outdoor flight tests. These results shed light on the avian use of wings and tails and offer an alternative design principle for drones with adaptive flight capabilities.

Phase Changing Materials-Based Variable-Stiffness Tensegrity Structures.

Soft robots leverage deformable bodies to achieve different types of locomotion, improve transportability, and safely navigate cluttered environments. In this context, variable-stiffness structures provide soft robots with additional properties, such as the ability to increase forces transmitted to the environment, to lock into different body configurations, and to reduce the number of actuators required for morphological change. Tensegrity structures have been recently proposed as a biologically inspired design principle for soft robots. However, the few examples of tensegrity structures with variable stiffness displayed relatively small stiffness change (i.e., by a factor of 3) or resorted to multiple and bulky actuators. In this article, we describe a novel design approach to variable-stiffness tensegrity structures (VSTSs) that relies on the use of variable-stiffness cables (VSCs). As an example, we describe the design and implementation of a three-strut tensegrity structure with VSCs made of low melting point alloys. The resulting VSTS displays unprecedented stiffness changes by a factor of 28 in compression and 13 in bending. We show the capabilities of the proposed VSTS in three validation scenarios with different tensegrity architectures: (1) a beam with tunable load-bearing capability, (2) a structure that can self-deploy and lock its shape in both deployed and undeployed states, and (3) a joint with underactuated shape deformations.

Stretchable pumps for soft machines.

Machines made of soft materials bridge life sciences and engineering1. Advances in soft materials have led to skin-like sensors and muscle-like actuators for soft robots and wearable devices1-3. Flexible or stretchable counterparts of most key mechatronic components have been developed4,5, principally using fluidically driven systems6-8; other reported mechanisms include electrostatic9-12, stimuli-responsive gels13,14 and thermally responsive materials such as liquid metals15-17 and shape-memory polymers18. Despite the widespread use of fluidic actuation, there have been few soft counterparts of pumps or compressors, limiting the portability and autonomy of soft machines4,8. Here we describe a class of soft-matter bidirectional pumps based on charge-injection electrohydrodynamics19. These solid-state pumps are flexible, stretchable, modular, scalable, quiet and rapid. By integrating the pump into a glove, we demonstrate wearable active thermal management. Embedding the pump in an inflatable structure produces a self-contained fluidic 'muscle'. The stretchable pumps have potential uses in wearable laboratory-on-a-chip and microfluidic sensors, thermally active clothing and autonomous soft robots.

Haptic Feedback Perception and Learning With Cable-Driven Guidance in Exosuit Teleoperation of a Simulated Drone.

Robotics teleoperation enables human operators to control the movements of distally located robots. The development of new wearable interfaces as alternatives to hand-held controllers has created new modalities of control, which are more intuitive to use. Nevertheless, such interfaces also require a period of adjustment before operators can carry out their tasks proficiently. In several fields of human-machine interaction, haptic guidance has proven to be an effective training tool for enhancing user performance. This paper presents the results of psychophysical and motor learning studies that were carried out with human participant to assess the effect of cable-driven haptic guidance for a task involving aerial robotic teleoperation. The guidance system was integrated into an exosuit, called the Flyjacket, that was developed to control drones with torso movements. Results for the just noticeable difference and from the Stevens Power Law suggest that the perception of force on the users' torso scales linearly with the amplitude of the force exerted through the cables and the perceived force is close to the magnitude of the stimulus. Motor learning studies reveal that this form of haptic guidance improves user performance in training, but this improvement is not retained when participants are evaluated without guidance.

The role of optic flow pooling in insect flight control in cluttered environments.

Flight through cluttered environments, such as forests, poses great challenges for animals and machines alike because even small changes in flight path may lead to collisions with nearby obstacles. When flying along narrow corridors, insects use the magnitude of visual motion experienced in each eye to control their position, height, and speed but it is unclear how this strategy would work when the environment contains nearby obstacles against a distant background. To minimise the risk of collisions, we would expect animals to rely on the visual motion generated by only the nearby obstacles but is this the case? To answer this, we combine behavioural experiments with numerical simulations and provide the first evidence that bumblebees extract the maximum rate of image motion in the frontal visual field to steer away from obstacles. Our findings also suggest that bumblebees use different optic flow calculations to control lateral position, speed, and height.

Data-driven body-machine interface for the accurate control of drones.

The accurate teleoperation of robotic devices requires simple, yet intuitive and reliable control interfaces. However, current human-machine interfaces (HMIs) often fail to fulfill these characteristics, leading to systems requiring an intensive practice to reach a sufficient operation expertise. Here, we present a systematic methodology to identify the spontaneous gesture-based interaction strategies of naive individuals with a distant device, and to exploit this information to develop a data-driven body-machine interface (BoMI) to efficiently control this device. We applied this approach to the specific case of drone steering and derived a simple control method relying on upper-body motion. The identified BoMI allowed participants with no prior experience to rapidly master the control of both simulated and real drones, outperforming joystick users, and comparing with the control ability reached by participants using the bird-like flight simulator Birdly.

Soft Biomimetic Fish Robot Made of Dielectric Elastomer Actuators.

This article presents the design, fabrication, and characterization of a soft biomimetic robotic fish based on dielectric elastomer actuators (DEAs) that swims by body and/or caudal fin (BCF) propulsion. BCF is a promising locomotion mechanism that potentially offers swimming at higher speeds and acceleration rates, and efficient locomotion. The robot consists of laminated silicone layers wherein two DEAs are used in an antagonistic configuration, generating undulating fish-like motion. The design of the robot is guided by a mathematical model based on the Euler-Bernoulli beam theory and takes account of the nonuniform geometry of the robot and of the hydrodynamic effect of water. The modeling results were compared with the experimental results obtained from the fish robot with a total length of 150 mm, a thickness of 0.75 mm, and weight of 4.4 g. We observed that the frequency peaks in the measured thrust force produced by the robot are similar to the natural frequencies computed by the model. The peak swimming speed of the robot was 37.2 mm/s (0.25 body length/s) at 0.75 Hz. We also observed that the modal shape of the robot at this frequency corresponds to the first natural mode. The swimming of the robot resembles real fish and displays a Strouhal number very close to those of living fish. These results suggest the high potential of DEA-based underwater robots relying on BCF propulsion, and applicability of our design and fabrication methods.

Soft Robotic Grippers.

Advances in soft robotics, materials science, and stretchable electronics have enabled rapid progress in soft grippers. Here, a critical overview of soft robotic grippers is presented, covering different material sets, physical principles, and device architectures. Soft gripping can be categorized into three technologies, enabling grasping by: a) actuation, b) controlled stiffness, and c) controlled adhesion. A comprehensive review of each type is presented. Compared to rigid grippers, end-effectors fabricated from flexible and soft components can often grasp or manipulate a larger variety of objects. Such grippers are an example of morphological computation, where control complexity is greatly reduced by material softness and mechanical compliance. Advanced materials and soft components, in particular silicone elastomers, shape memory materials, and active polymers and gels, are increasingly investigated for the design of lighter, simpler, and more universal grippers, using the inherent functionality of the materials. Embedding stretchable distributed sensors in or on soft grippers greatly enhances the ways in which the grippers interact with objects. Challenges for soft grippers include miniaturization, robustness, speed, integration of sensing, and control. Improved materials, processing methods, and sensing play an important role in future research.

Spatial Encoding of Translational Optic Flow in Planar Scenes by Elementary Motion Detector Arrays.

Elementary Motion Detectors (EMD) are well-established models of visual motion estimation in insects. The response of EMDs are tuned to specific temporal and spatial frequencies of the input stimuli, which matches the behavioural response of insects to wide-field image rotation, called the optomotor response. However, other behaviours, such as speed and position control, cannot be fully accounted for by EMDs because these behaviours are largely unaffected by image properties and appear to be controlled by the ratio between the flight speed and the distance to an object, defined here as relative nearness. We present a method that resolves this inconsistency by extracting an unambiguous estimate of relative nearness from the output of an EMD array. Our method is suitable for estimation of relative nearness in planar scenes such as when flying above the ground or beside large flat objects. We demonstrate closed loop control of the lateral position and forward velocity of a simulated agent flying in a corridor. This finding may explain how insects can measure relative nearness and control their flight despite the frequency tuning of EMDs. Our method also provides engineers with a relative nearness estimation technique that benefits from the low computational cost of EMDs.

Bioinspired dual-stiffness origami.

Origami manufacturing has led to considerable advances in the field of foldable structures with innovative applications in robotics, aerospace, and metamaterials. However, existing origami are either load-bearing structures that are prone to tear and fail if overloaded or resilient soft structures with limited load capability. In this manuscript, we describe an origami structure that displays both high load bearing and high resilience characteristics. The structure, which is inspired by insect wings, consists of a prestretched elastomeric membrane, akin to the soft resilin joints of insect wings, sandwiched between rigid tiles, akin to the rigid cuticles of insect wings. The dual-stiffness properties of the proposed structure are validated by using the origami as an element of a quadcopter frame that can withstand aerodynamic forces within its flight envelope but softens during collisions to avoid permanent damage. In addition, we demonstrate an origami gripper that can be used for rigid grasping but softens to avoid overloading of the manipulated objects.

Forceful manipulation with micro air vehicles.

Micro air vehicles (MAVs) are finding use across an expanding range of applications. However, when interacting with the environment, they are limited by the maximum thrust they can produce. Here, we describe FlyCroTugs, a class of robots that adds to the mobility of MAVs the capability of forceful tugging up to 40 times their mass while adhering to a surface. This class of MAVs, which finds inspiration in the prey transportation strategy of wasps, exploits controllable adhesion or microspines to firmly adhere to the ground and then uses a winch to pull heavy objects. The combination of flight and adhesion for tugging creates a class of 100-gram multimodal MAVs that can rapidly traverse cluttered three-dimensional terrain and exert forces that affect human-scale environments. We discuss the energetics and scalability of this approach and demonstrate it for lifting a sensor into a partially collapsed building. We also demonstrate a team of two FlyCroTugs equipped with specialized end effectors for rotating a lever handle and opening a heavy door.

Disentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies.

Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.