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Existing technologies for patients with respiratory insufficiency have focused on providing reliable assistance in their breathing. However, the need for assistance in other everyday respiratory functions, such as coughing and speaking, has remained unmet in these patients. Here, we propose Exo-Abs, a wearable robotic system that can universally assist wide-ranging respiratory functions by applying compensatory force to a user's abdomen in synchronization with their air usage. Inspired by how human abdominal muscles transmit pressure to the lungs via abdominal cavity compression, a biomechanically interactive platform was developed to optimally utilize the abdominal compression while aligning the assistance with a user's spontaneous respiratory effort. In addition to the compact form factor, thorough analytic procedures are described as initial steps toward taking the human respiratory system into the scope of robotics technology. We demonstrate the validity of the overall human–system interaction with the assistance performance under three essential respiratory functions: breathing, coughing, and speaking. Our results show that the system can significantly improve the performance of all these functions by granting on-demand and self-reliant assistance to its users. [ABSTRACT FROM AUTHOR]

Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this article, we propose a practical and theoretically justified approach to maintain safety in the presence of dynamics uncertainty. Our approach leverages Bayesian meta-learning with last-layer adaptation. The expressiveness of neural-network features trained offline, paired with efficient last-layer online adaptation, enables the derivation of tight confidence sets, which contract around the true dynamics as the model adapts online. We exploit these confidence sets to plan trajectories that guarantee the safety of the system. Our approach handles problems with high dynamics uncertainty, where reaching the goal safely is potentially initially infeasible, by first exploring to gather data and reduce uncertainty, before autonomously exploiting the acquired information to safely perform the task. Under reasonable assumptions, we prove that our framework guarantees the high-probability satisfaction of all constraints at all times jointly, i.e., over the total task duration. This theoretical analysis also motivates two regularizers of last-layer meta-learning models that improve online adaptation capabilities as well as performance by reducing the size of the confidence sets. We extensively demonstrate our approach in simulation and on hardware. [ABSTRACT FROM AUTHOR]

Legged robots are very promising for use in real-world applications, but their operation in narrow spaces is still challenging. One solution for enhancing their environmental adaptability is to design a small-sized biomimetic robot capable of performing multiple motions. By capturing a decent representation of an actual rat (rattus norvegicus), we developed a small-sized quadruped robotic rat (SQuRo), which includes four limbs and one flexible spine. On the basis of the extracted key movement joints, SQuRo was subtly designed with a relatively elongated slim body (aspect ratio: 3.42) and smaller weight (220 g) compared with quadruped robots of the same scale. Accordingly, we propose a control framework for multimodal motion planning, and the appropriate control parameters were tuned through optimization with consideration to the stability and actuation limits. The results obtained through a series of experimental tests reveal that SQuRo achieves a superior motion performance compared with existing state-of-the-art small-sized quadruped robots. Remarkably, SQuRo has an extremely small turning radius (0.48 BL) and strong payload capacity (200 g), and it can recover from falls. [ABSTRACT FROM AUTHOR]

The robotic manipulation of deformable linear objects is a classic and challenging topic. Apart from synthetic objects, such as wires and cables, linear objects are also commonly found in biological cells and organisms. Biomanipulation of such objects is hampered by difficulties, such as limited degrees of freedom of micromanipulators and varied mechanical properties of the biological entities to manipulate. This article presents a robotic manipulation of human sperm, which are deformable cells with a linear shape. The shape and movement of the cell are recapitulated by our developed geometric and kinematic models. Under unfixed constraints between the end-effector and the cell, path planning is designed to update the manipulation point to control cell deformation. A state transition function is formulated in path planning to handle the stiffness variations of sperm without force sensing. A model-predictive controller is designed to minimize the orientation error and manipulation path length. To detect sperm tail for visual feedback, an accuracy of 98% was achieved via deep neural networks. The robotic manipulation of human sperm was performed using a standard clinical setup of a glass micropipette to rotate a sperm to the target orientation. Experimental results showed that robotic sperm rotation achieved an orientation error of 0.8 $^{\circ }$ , a tail curvedness of 0.14 $\mu$ m $^{-1}$ , and an operation time of 5.6 s, all significantly less than those of the manual approach. The less orientation error and tail curvedness after robotic rotation led to a significantly lower speed of sperm entering the micropipette during sperm aspiration, resulting in a higher success rate of 97% (versus 76% after manual rotation) for aspiration control. [ABSTRACT FROM AUTHOR]

The ability to grasp a wider range of objects in size and shape directly relates to the performance of robotic grippers. Adapting to complex geometries of objects requires large degrees of freedom to allow complex configurations. However, complexity in controlling many individual joints leads to introduction of underactuated mechanisms, in which traditional finger designs composed of revolute joints allow only flexion/extension motions. In this article, we propose a length-adjustable linkage mechanism in the underactuated finger controlled by an antagonistic tendon pair. The resulting gripper can elongate the fingers for an increased task space or shorten them for a finer spatial resolution. For tactile sensing, hyperelastic soft sensors are used to stretch with finger elongation. Contact pressures measured by the soft sensors are used in force-feedback control for which either the joint angles or the link lengths are adjusted. Lastly, a multimodal control scheme that combines elongation and flexion modes is demonstrated with tasks of dexterous manipulation. [ABSTRACT FROM AUTHOR]

In this article, we holistically present a hybrid-linear inverted pendulum (H-LIP) based approach for synthesizing and stabilizing 3-D foot-underactuated bipedal walking, with an emphasis on thorough hardware realization. The H-LIP is proposed to capture the essential components of the underactuated and actuated part of the robotic walking. The robot walking gait is then directly synthesized based on the H-LIP. We comprehensively characterize the periodic orbits of the H-LIP and provably derive the stepping stabilization via its step-to-step (S2S) dynamics, which is then utilized to approximate the S2S dynamics of the horizontal state of the center of mass of the robotic walking. The approximation facilities a H-LIP based stepping controller to provide desired step sizes to stabilize the robotic walking. By realizing the desired step sizes, the robot achieves dynamic and stable walking. The approach is fully evaluated in both simulation and experiment on the 3-D underactuated bipedal robot Cassie, which demonstrates dynamic walking behaviors with both high versatility and robustness. [ABSTRACT FROM AUTHOR]

Precise rotational positioning of end-effectors under microscopy is crucial for robotic micromanipulation. However, the end-effector is presently limited to a fixed orientation, which is manually set before a given micromanipulation task, lacking accuracy and versatility of in situ reorientation. In this article, we present a unified framework for rotationally positioning the end-effector in three dimensions by establishing a general rotational model, developing a detection method within the limited field of view under microscopy, and designing a three-loop control strategy that adapts to different experimental requirements and model parameters. In experiments, a standard angled micropipette was used as the end-effector to verify the validity of the proposed methods. The performance was evaluated experimentally where the micropipette was robotically rotated to an arbitrarily desired orientation with an average orientation error less than $2^\circ$. In the experiments of sperm manipulation, the in situ micropipette orientation control capability improved the success rate of sperm immobilization and achieved dexterous robotic sperm orientation for facile aspiration. [ABSTRACT FROM AUTHOR]

Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real-world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We define a specification language, capability temporal logic, to describe rich, temporal properties involving tasks requiring the participation of multiple agents with multiple capabilities, e.g., sensors or end effectors. Arbitrary missions and team dynamics are jointly encoded as constraints in a mixed integer linear program, and solved efficiently using commercial off-the-shelf solvers. ScRATCHeS optionally allows optimization for maximal robustness to agent attrition at the penalty of increased computation time. We include an online replanning algorithm that adjusts the plan after an agent has dropped out. The flexible specification language, fast solution time, and optional robustness of ScRATCHeS provide a first step toward a multipurpose on-the-fly planning tool for tasking large teams of agents with multiple capabilities enacting missions with multiple tasks. We present randomized computational experiments to characterize scalability and hardware demonstrations to illustrate the applicability of our methods. [ABSTRACT FROM AUTHOR]

This article presents a software platform tailored for prehensile manipulation planning named humanoid path planner. The platform implements an original way of modeling manipulation planning through a constraint graph that represents the numerical constraints that define the manipulation problem. We propose an extension of the RRT algorithm to manipulation planning that is able to solve a large variety of problems. We provide replicable experimental results via a docker image that readers may download to run the experimental results by themselves. [ABSTRACT FROM AUTHOR]

Autonomous robots frequently need to detect “interesting” scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require fast deployment with little or no training data. Prior work considers “interestingness” based on data from the same distribution. Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly. To address this problem, we develop a novel translation-invariant visual memory and design a three-stage architecture for long-term, short-term, and online learning, which enables the system to learn human-like experience, environmental knowledge, and online adaption, respectively. With this system, we achieve an average of 20% higher accuracy than the state-of-the-art unsupervised methods in a subterranean tunnel environment. We show comparable performance to supervised methods for robot exploration scenarios showing the efficacy of our approach. We expect that the presented method will play an important role in the robotic interestingness recognition exploration tasks. [ABSTRACT FROM AUTHOR]

Microrobots (MRs) have attracted growing interest for their potentialities in diagnosis and noninvasive intervention in hard-to-reach body areas. The safe operation of biomedical MRs requires fine control capabilities, which strongly depend on precise and robust feedback about their position over time. Ultrasound acoustic phase analysis (US-APA) may allow for a reliable feedback strategy for MR imaging and tracking in tissue. In this article, we combine task-specific magnetic actuation and related US-APA motion tracking to achieve closed-loop navigation of a magnetic MR, rolling on the boundary of a lumen in a tissue-mimicking phantom. A C-arm system attached to a robotic platform is used to precisely position the magnetic actuation source and US-APA detection unit within the workspace, thus enabling MR visual-servoing. In the first place, the proposed approach allows to perform supervised localization of the MR without any a-priori knowledge of its position. After localization, a robust real-time tracking enables closed-loop MR teleoperation in the phantom lumina over a travel distance of 80 mm (145 body lengths), both in static and counter flow, thus achieving an average position tracking error of 368 micron (0.67 body lengths). For the first time, our results validate US-APA as a reliable feedback strategy for visual-servoing control of MRs in simulated in-body environment. [ABSTRACT FROM AUTHOR]

Industrial robots are characterized by good flexibility and a large working space, and offer a new approach for the machining of large and complex parts with small machining allowances (extra material allowed for subsequent machining). Parts of this type (such as aircraft skin parts, wind turbine blades, etc.) are easily deformed due to their large scale and low stiffness. Therefore, these parts cannot be directly machined according to the designed model. A feasible method is to plan a robotic machining path by using the point clouds of parts after clamping from onsite measurement which contains inherent defects of measurement such as noise points and abrupt points. In this article, a novel method is proposed to plan and optimize a robotic machining path that meets the requirements of smoothness, dexterity, and stiffness based on the point cloud from onsite measurement. The dual nonuniform rational B-spline curves of the machining path points and tool axis points are generated at first. Next, an objective function of smoothness optimization is established to filter out the local mutation of the path by considering the constraints of both the deformation energy and the deviation. Then, the objective function of robot postures optimization is established to optimize dexterity and Cartesian stiffness of a robot during the machining process. To show the feasibility of the proposed method, simulation and experiments are carried out. It is proved that the proposed method can generate a smooth machining trajectory. The stability of joint rotation and the rigidity and dexterity of the robot are improved during the machining process. [ABSTRACT FROM AUTHOR]

Proximity perception is a technology that has the potential to play an essential role in the future of robotics. It can fulfill the promise of safe, robust, and autonomous systems in industry and everyday life, alongside humans, as well as in remote locations in space and underwater. In this survey article, we cover the developments of this field from the early days up to the present, with a focus on human-centered robotics. In this domain, proximity sensors are typically deployed in two scenarios: first, on the exterior of manipulator arms to support safety and interaction functionality, and second, on the inside of grippers or hands to support grasping and exploration. Therefore, based on this observation, in the beginning of this article, we propose a categorization to organize the use cases of proximity sensors in human-centered robotics. Then, we devote effort to present the sensing technologies and different measuring principles that have been developed over the years, also providing a summary in form of a table. Following, we review the literature regarding the applications that have been proposed. Finally, we give an overview of the most important trends that will shape the future of this domain. [ABSTRACT FROM AUTHOR]

This article presents an analytical method to modulate the dynamic response of a robotic manipulator interacting with its environment when performing impedance-related robotic tasks, through the choice of stiffness and damping parameters. By joint space analysis of vibration and experiments, we prove that in order to preserve a desired dynamic behavior of the robot in the Cartesian space, neither a stiffness nor damping matrix can be arbitrarily chosen; this choice has to meet the desired dynamic criteria for any given configuration. After mapping the parameters (matrices) into the joint space, we analyze the vibratory dynamics of the robot and identify the proper way of suppressing of the vibration modes by specific elements in the Cartesian damping matrix without need of trial and error. Our method is especially useful for redundant robots. We show and compare experimental results from two different 7 degrees of freedom robotic manipulators. [ABSTRACT FROM AUTHOR]

Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This article presents the overall system—design, hardware, and software—for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating noncontact human–robot interaction (HRI) research. The design of the system is motivated by feedback sampled from the HRI research community. The overall design maintains a balance of affordability and functionality. Initial Quori testing and a six-month deployment are presented. Ten Quori platforms have been awarded to a diverse group of researchers from across the United States to facilitate HRI research to build a community database from a common platform. [ABSTRACT FROM AUTHOR]

This article develops a flexible and robust robotic system for autonomously drawing on 3-D surfaces. The system takes 2-D drawing strokes and a 3-D target surface (mesh or point clouds) as input. It maps the 2-D strokes onto the 3-D surface and generates a robot motion to draw the mapped strokes using visual recognition, grasp pose reasoning, and motion planning. The system is flexible compared to conventional robotic drawing systems as we do not fix drawing tools to the end of a robot arm. Instead, a robot recognizes and picks up pens online and holds the pens to draw 3-D strokes. Meanwhile, the system has high robustness thanks to the following crafts: First, a high-quality mapping method is developed to minimize deformation in the strokes. Second, visual detection is used to reestimate the drawing tool’s pose before executing each drawing motion. Third, force control is employed to compensate for noisy visual detection and calibration and ensure a firm touch between the pen tip and the surface. Fourth, error detection and recovery are implemented to deal with slippage and other anomalies. The planning and executions are performed in a closed-loop manner until the strokes are successfully drawn. We evaluate the system and analyze the necessity of the various crafts using different real-world tasks. The results show that the proposed system is flexible and robust to generate robotic motion that picks up the pens and successfully draws 3-D strokes on given surfaces. [ABSTRACT FROM AUTHOR]

Anytime a robot manipulator is controlled via visual feedback, the transformation between the robot and camera frame must be known. However, in the case where cameras can only capture a portion of the robot manipulator in order to better perceive the environment being interacted with, there is greater sensitivity to errors in calibration of the base-to-camera transform. A secondary source of uncertainty during robotic control are inaccuracies in joint angle measurements which can be caused by biases in positioning and complex transmission effects such as backlash and cable stretch. In this work, we bring together these two sets of unknown parameters into a unified problem formulation when the kinematic chain is partially visible in the camera view. We prove that these parameters are nonidentifiable implying that explicit estimation of them is infeasible. To overcome this, we derive a smaller set of parameters we call lumped error since it lumps together the errors of calibration and joint angle measurements. A particle filter method is presented and tested in simulation and on two real world robots to estimate the lumped error and show the efficiency of this parameter reduction. [ABSTRACT FROM AUTHOR]

Twisted and coiled actuators (TCAs) have recently emerged as a promising artificial muscle for various robotic applications because they are strong, low cost, and customizable. To better facilitate the applications, it is critical to establish general and precise models for different types of TCAs (e.g., self-coiled, free-stroke, conical, etc.). Although several modeling methods have been proposed recently, existing models either fail to capture the nonlinearity during large deformations or cannot model TCAs with nonuniform geometries. In this work, we establish a general framework for modeling TCAs using Cosserat rod theory that can capture the nonlinearity of large deformations and simulate TCAs with nonuniform geometries. Furthermore, we show existing methods are special cases of our general model. Comprehensive statics and dynamics experiments are conducted to verify the proposed model, and the results demonstrate that the model is more accurate than existing ones, especially when a TCA is subject to large deformations. Given the wide applications of TCAs, our general model can help to better design, optimize, and control systems/robots/devices driven by different types of TCAs. [ABSTRACT FROM AUTHOR]

A control design framework for the coupled stability problem in robotics is presented. The proposed framework fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction where both the manipulator and the environment are dissipative systems with quadratic supply rates. In contrast with existing results, the proposed framework can be used in combination with an arbitrary robot’s tracking control algorithm, and its stabilizing action when in contact with environment does not affect the robot’s trajectory tracking performance in free space. The framework is based on the recently developed nonplanar conic systems formalism and generalized scattering-based stabilization methods. A detailed design example is presented which illustrates the capabilities of the proposed method. [ABSTRACT FROM AUTHOR]

In robots, nonprehensile manipulation operations such as pushing are a useful way of moving large, heavy, or unwieldy objects, moving multiple objects at once, or reducing uncertainty in the location or pose of objects. In this study, we propose a reactive and adaptive method for robotic pushing that uses rich feedback from a high-resolution optical tactile sensor to control push movements instead of relying on analytical or data-driven models of push interactions. Specifically, we use goal-driven tactile exploration to actively search for stable pushing configurations that cause the object to maintain its pose relative to the pusher while incrementally moving the pusher and object toward the target. We evaluate our method by pushing objects across planar and curved surfaces. For planar surfaces, we show that the method is accurate and robust to variations in initial contact position/angle, object shape, and start position; for curved surfaces, the performance is degraded slightly. An immediate consequence of our work is that it shows that explicit models of push interactions might be sufficient but are not necessary for this type of task. It also raises the interesting question of which aspects of the system should be modeled to achieve the best performance and generalization across a wide range of scenarios. Finally, it highlights the importance of testing on nonplanar surfaces and in other more complex environments when developing new methods for robotic pushing. [ABSTRACT FROM AUTHOR]

Understanding how people are likely to move is key to efficient and safe robot navigation in human environments. However, mobile robots can only observe a fraction of the environment at a time, while the activity patterns of people may also change at different times. This article introduces a new methodology for mobile robot exploration to maximize the knowledge of human activity patterns by deciding where and when to collect observations. We introduce an exploration policy driven by the entropy levels in a spatio-temporal map of pedestrian flows, and compare multiple spatio-temporal exploration strategies including both informed and uninformed approaches. The evaluation is performed by simulating mobile robot exploration using real sensory data from three long-term pedestrian datasets. The results show that for certain scenarios the models built with proposed exploration system can better predict the flow patterns than uninformed strategies, allowing the robot to move in a more socially compliant way, and that the exploration ratio is a key factor when it comes to the model prediction accuracy. [ABSTRACT FROM AUTHOR]

Multidomain activities that incorporate physical, cognitive, and social stimuli can enhance older adults’ overall health and quality of life. Several robotic platforms have been developed to provide these therapies in a quantifiable manner to complement healthcare personnel in resource-strapped long-term care settings. However, these platforms are primarily limited to one-to-one human–robot interaction (HRI) and thus do not enhance social interaction. In this article, we present a novel HRI framework and a realized platform called socially assistive robotic (SAR)-Connect to foster robot-mediated social interaction among older adults through carefully designed tasks that also incorporate physical and cognitive stimuli. SAR-Connect seamlessly integrates a humanoid robot with a virtual reality-based activity platform and a multimodal data acquisition module including game interaction, audio, visual, and electroencephalography responses of the participants. Results from a laboratory-based user study with older adults indicates the potential of SAR-Connect that showed this system could 1) involve one or multiple older adults to perform multidomain activities and provide dynamic guidance, 2) engage them in the robot-mediated task and foster human–human interaction, and 3) quantify their social and activity engagement from multiple sensory modalities. [ABSTRACT FROM AUTHOR]

This article presents a back-drivable spherical wrist with smart compliance for rendering human wrist-like capabilities in robotic applications. In order to synthetically impose both motion accuracy and active compliance for dedicated wrist manipulations, a robust hybrid control method is proposed that can switch between motion control (at free motion) and impedance control (at interactions with the environment). A sliding manifold is formulated so that when the control law drives the sliding manifold to zero, the motion/impedance errors will automatically converge to bounded regions despite system uncertainties/inaccuracies. Due to the direct-drive configuration of the proposed spherical wrist, the contact torque can be precisely estimated with an observer, providing an intuitive criterion of contact based on which the control modes can switch autonomously without external force/torque sensors and predefined geometric boundaries that are usually required in conventional hybrid force/position control. The concept is validated on a spherical wrist test-bed in the context of a throat swabbing process. The performance of the hybrid motion-impedance control evaluated against conventional impedance and admittance control methods demonstrate that the proposed spherical wrist enables a robotic system with both fine manipulations and smooth compliant interactions in wrist space. [ABSTRACT FROM AUTHOR]

Controlling biomimetic underwater robots in unknown flow fields remains a challenge due to the strong nonlinearity of the fluid. This article investigates the attitude holding task of a robotic fish swimming in reality. Such a typical sensing-based control task requires the fish to keep a desired angle of attack in an unknown and even varied incoming flow. To this end, we propose a learning-based approach by using a deep neural network directly maps the raw data of sensors equipped on the robot to the continuous control signals in an end-to-end manner. First, based on experimental data of the physical robot, we construct a data-driven simulation environment including three modules of dynamic, sensor, and control. The dynamic and sensor modules are established to model the dynamics of the fish and to generate its sensors’ data, based on which a deep reinforcement learning (DRL) algorithm in the control module is trained to get a control policy. Then, we directly deploy the trained policy to a physical robotic fish for attitude holding task. Experimental results demonstrate the robustness and effectiveness of the DRL policy and, thus, verify the success of our approach to achieving sim-to-real transfer. [ABSTRACT FROM AUTHOR]

Recent progress in the field of robotic manipulation has generated interest in fully automatic object packing in warehouses. This article presents a formulation of the robotic packing problem that ensures stability of the object pile during packing and the feasibility of the robot motion while maximizing packing density. A constructive packing algorithm is proposed to address this problem by searching over the space of item positions and orientations. Moreover, a new heightmap minimization heuristic is shown to outperform existing heuristics in the literature in the presence of nonconvex objects. Two strategies for improving the robustness of executed packing plans are also proposed: 1) conservative planning ensures plan feasibility under uncertainty in model parameters; and 2) closed-loop packing uses vision sensors to measure placement errors and replans to correct for them. The proposed planner and error mitigation strategies are evaluated in simulation and on a state-of-the-art physical packing testbed. Experiments demonstrate that the proposed planner generates high-quality packing plans, and the error mitigation strategies improve success rates beyond an open-loop baseline from 83% to 100% on five-item orders. [ABSTRACT FROM AUTHOR]

Prokaryotic flagellum is considered as the only known example of a biological “wheel,” a system capable of converting the action of rotatory actuator into a continuous propulsive force. For this reason, flagella are an interesting case study in soft robotics and they represent an appealing source of inspiration for the design of underwater robots. A great number of flagellum-inspired devices exists, but these are all characterized by a size ranging in the micrometer scale and mostly realized with rigid materials. Here, we present the design and development of a novel generation of macroscale underwater propellers that draw their inspiration from flagellated organisms. Through a simple rotatory actuation and exploiting the capability of the soft material to store energy when interacting with the surrounding fluid, the propellers attain different helical shapes that generate a propulsive thrust. A theoretical model is presented, accurately describing and predicting the kinematic and the propulsive capabilities of the proposed solution. Different experimental trials are presented to validate the accuracy of the model and to investigate the performance of the proposed design. Finally, an underwater robot prototype propelled by four flagellar modules is presented. [ABSTRACT FROM AUTHOR]

While traditional robotic systems come with a monolithic system design, reconfigurable multirobot systems can share and shift physical resources in an on-demand fashion. Multirobot operations can benefit from this flexibility by actively managing system redundancies depending on current tasks and having more options to respond to failure events. To support this active exploitation of redundancies in robotic systems, this article details an organization model as basis for planning with reconfigurable multirobot systems. The model allows us to exploit redundancies when optimizing a multirobot system’s probability of survival with respect to a desired mission. The resulting planning approach trades safety against efficiency in robotic operations and thereby offers a new perspective and tool to design and improve multirobot missions. We use a simulated multirobot planetary exploration mission to evaluate this approach and highlight an exemplary performance landscape. Our implementation of the organization model is open-source available (https://github.com/rock-knowledge-reasoning/knowledge-reasoning-moreorg). [ABSTRACT FROM AUTHOR]

This article analyzes the evolution and current trends in aerial robotic manipulation, comprising helicopters, conventional underactuated multirotors, and multidirectional thrust platforms equipped with a wide variety of robotic manipulators capable of physically interacting with the environment. It also covers cooperative aerial manipulation and interconnected actuated multibody designs. The review is completed with developments in teleoperation, perception, and planning. Finally, a new generation of aerial robotic manipulators is presented with our vision of the future. [ABSTRACT FROM AUTHOR]

Personalizing medical devices such as lower limb wearable robots is challenging. While the initial feasibility of automating the process of knee prosthesis control parameter tuning has been demonstrated in a principled way, the next critical issue is to improve tuning efficiency and speed it up for the human user, in clinic settings, while maintaining human safety. We, therefore, propose a policy iteration with constraint embedded (PICE) method as an innovative solution to the problem under the framework of reinforcement learning. Central to PICE is the use of a projected Bellman equation with a constraint of assuring positive semidefiniteness of performance values during policy evaluation. Additionally, we developed both online and offline PICE implementations that provide additional flexibility for the designer to fully utilize measurement data, either from on-policy or off-policy, to further improve PICE tuning efficiency. Our human subject testing showed that the PICE provided effective policies with significantly reduced tuning time. For the first time, we also experimentally evaluated and demonstrated the robustness of the deployed policies by applying them to different tasks and users. Putting it together, our new way of problem solving has been effective as PICE has demonstrated its potential toward truly automating the process of control parameter tuning for robotic knee prosthesis users. [ABSTRACT FROM AUTHOR]

This article presents a generalizable methodology for data-driven identification of nonlinear dynamics that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states. Using higher order derivatives of general nonlinear dynamics that need not be known, we construct a Koopman-operator-based linear representation and utilize Taylor series accuracy analysis to derive an error bound. The resulting error formula is used to choose the order of derivatives in the basis functions and obtain a data-driven Koopman model using a closed-form expression that can be computed in real time. Using the inverted pendulum system, we illustrate the robustness of the error bounds given noisy measurements of unknown dynamics, where the derivatives are estimated numerically. When combined with control, the Koopman representation of the nonlinear system has marginally better performance than competing nonlinear modeling methods, such as SINDy and NARX. In addition, as a linear model, the Koopman approach lends itself readily to efficient control design tools, such as linear–quadratic regulator, whereas the other modeling approaches require nonlinear control methods. The efficacy of the approach is further demonstrated with simulation and experimental results on the control of a tail-actuated robotic fish. Experimental results show that the proposed data-driven control approach outperforms a tuned proportional–integral–derivative controller and that updating the data-driven model online significantly improves performance in the presence of unmodeled fluid disturbance. This article is complemented with a video available at https://youtu.be/9_wx0tdDta0. [ABSTRACT FROM AUTHOR]

This article surveys the literature on human–robot object handovers. A handover is a collaborative joint action, where an agent, the giver, gives an object to another agent, the receiver. The physical exchange starts when the receiver first contacts the object held by the giver and ends when the giver fully releases the object to the receiver. However, important cognitive and physical processes begin before the physical exchange, including initiating implicit agreement with respect to the location and timing of the exchange. From this perspective, we structure our review into the two main phases delimited by the aforementioned events: a prehandover phase and the physical exchange. We focus our analysis on the two actors (giver and receiver) and report the state of the art of robotic givers (robot-to-human handovers) and the robotic receivers (human-to-robot handovers). We report a comprehensive list of qualitative and quantitative metrics commonly used to assess the interaction. While focusing our review on the cognitive level (e.g., prediction, perception, motion planning, and learning) and the physical level (e.g., motion, grasping, and grip release) of the handover, we also discuss safety. We compare the behaviors displayed during human-to-human handovers to the state of the art of robotic assistants and identify the major areas of improvement for robotic assistants to reach performance comparable to human interactions. Finally, we propose a minimal set of metrics that should be used in order to enable a fair comparison among the approaches. [ABSTRACT FROM AUTHOR]

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats intermodular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks. [ABSTRACT FROM AUTHOR]

Dynamic control of robotic exoskeletons is paramount to ensuring safe, synergistic assistive action of functional benefit to users. To date, exoskeleton controllers have excelled in rhythmic and quasi-rhythmic tasks, whereas control methods for assisting discrete movements remain limited by their task-specificity. Inspired by neurophysiological dynamic movement primitives (DMPs), we formulated a novel controller that facilitated a variety of lifting movements using a single adaptive DMP (aDMP), for wearable robotic assistance of discrete movements. For a variety of load lifting tasks, we first benchmarked our method's trajectory prediction accuracy against the state-of-the-art DMP using passively recorded exoskeleton sensor data (offline), followed by a functional validation of online aDMP trajectory estimates. Finally, we assessed the functional effects of aDMP-based exoskeletal assistance on joint kinematics and muscular activity during repetitive lifting. The new aDMP method accurately predicted and smoothly synchronized robotic assistance with variable movement trajectories, resulting in reduced muscular activation of the erector spinae muscles (up to 47.6%) while preserving lower-limb joint kinematics and reducing the extension time by 15.5% compared to unassisted conditions. This method holds promise for use in a wide range of wearable robotic applications, including both clinical rehabilitation and user assistance in activities of daily living and/or manual labor. [ABSTRACT FROM AUTHOR]

In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of large-scale real-world dataset required for this task typically does not exist for most robotic settings, which motivates the use of synthetic data. Our proposed method, unseen object instance segmentation (UOIS)-Net, separately leverages synthetic RGB and synthetic depth for unseen object instance segmentation. UOIS-Net is composed of two stages: first, it operates only on depth to produce object instance center votes in 2D or 3D and assembles them into rough initial masks. Second, these initial masks are refined using RGB. Surprisingly, our framework is able to learn from synthetic RGB-D data where the RGB is nonphotorealistic. To train our method, we introduce a large-scale synthetic dataset of random objects on tabletops. We show that our method can produce sharp and accurate segmentation masks, outperforming state-of-the-art methods on unseen object instance segmentation. We also show that our method can segment unseen objects for robot grasping. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) based tactile sensor offers significant benefits on practical deployment because of its sparse electrode allocation, including durability, large-area scalability, and low fabrication cost, but the degradation of a tactile spatial resolution has remained challenging. This article describes a deep neural network based EIT reconstruction framework, the EIT neural network (EIT-NN), alleviating this tradeoff between tactile sensing performance and hardware simplicity. EIT-NN learns a computationally efficient, nonlinear reconstruction attribute, achieving high-resolution tactile sensation and well-generalized reconstruction capability to address arbitrary complex touch modalities. We train EIT-NN by presenting a sim-to-real dataset synthesis strategy for computationally efficient generalizability. Furthermore, we propose a spatial sensitivity aware mean-squared error loss function, which uses an intrinsic spatial sensitivity of the sensor to guarantee a well-posed EIT operation. We validate an outperformance of EIT-NN against conventional EIT sensing methods by conducting a simulation study, a single-touch indentation test, and a two-point discrimination test. The results show improved spatial resolution, sensitivity, and localization accuracy. The beneficial features of the generalized sensing of EIT-NN were demonstrated by examining touch modality discrimination performance. [ABSTRACT FROM AUTHOR]

Tracking 6-D poses of objects from videos provides rich information to a robot in performing different tasks such as manipulation and navigation. In this article, we formulate the 6-D object pose tracking problem in the Rao–Blackwellized particle filtering framework, where the 3-D rotation and the 3-D translation of an object are decoupled. This factorization allows our approach, called PoseRBPF, to efficiently estimate the 3-D translation of an object along with the full distribution over the 3-D rotation. This is achieved by discretizing the rotation space in a fine-grained manner and training an autoencoder network to construct a codebook of feature embeddings for the discretized rotations. As a result, PoseRBPF can track objects with arbitrary symmetries while still maintaining adequate posterior distributions. Our approach achieves state-of-the-art results on two 6-D pose estimation benchmarks. We open-source our implementation at https://github.com/NVlabs/PoseRBPF. [ABSTRACT FROM AUTHOR]

This article presents a novel recursive smooth trajectory (RST) generation algorithm for application in robotics and in particular for unmanned aerial vehicles (UAVs). RST builds the trajectory recursively as a smooth polynomial path, thus a closed form trajectory satisfying any arbitrary dynamic limitation that translates into kinematic constraints (e.g., position, velocity, acceleration, etc.). Uncertainties and perturbations in the constraints are also discussed, modeled, and finally included in the calculation of the polynomial trajectory coefficients. Moreover, due to its recursive formulation, RST enables an immediate extension toward the solution of a trajectory optimization problem. In particular, it is shown that the minimum-snap piecewise polynomial trajectory can be interpreted as a special case of RST. The effectiveness of the proposed algorithm is demonstrated numerically via two illustrative scenarios. Its application to a UAV structure is also discussed to highlight the advantage of a smooth path over a piecewise one. Finally, the computational complexity and memory requirements are analyzed. [ABSTRACT FROM AUTHOR]

Numerous biological studies of body and/or caudal fin fish have powerfully identified that the fish body's passive properties, such as stiffness and its distribution, contribute to the fish's outstanding swimming performance. In this study, we develop a free-swimming tensegrity fish robot (TenFiBot) to help scholars experimentally improve their understanding of the role of stiffness distribution in fish-like swimming. We detail the overall structure and parameters of TenFiBot, which can achieve body stiffness distribution with high stiffness controllability over a wide range. The effective variable stiffness principle is based on antagonistic stiffness that results from the prestress of tension structures in a kinematically singular configuration. We validate the effectiveness of variable stiffness for one variable-stiffness tensegrity joint (VSTJ) and demonstrate that the virtual rotational centers are always in the acceptable range during stiffness variation. We exemplify some cases of the stiffness distribution to verify the validity of body stiffness variation. The experimental results show that swimming performance, such as swimming velocity, varied dramatically when the stiffness distribution was varied for the robotic fish. The swimming performance of the robotic fish can be enhanced tremendously by changing the stiffness distribution. Moreover, the VSTJs in the fish body have great potential in constructing compliant robotic arms and snake-like robots. [ABSTRACT FROM AUTHOR]

This article presents a unified mathematical approach for modeling, identifying, and solving the friction-inclusive dynamics of robotic mechanisms driven by sliding contact transmission systems. This approach is applied to the most common industrial screw-based drives: 1) the worm drive, 2) the simple lead screw drive, and 3) the antibacklash lead screw drive. The resulting dynamics, handily transferable to single and multiple degree of freedom (DoF) manipulators, are complemented with an algorithm for the solution of the forward dynamics problem as well as a framework for friction identification based on nonlinear optimization. The presented theory is experimentally tested on single-DoF screw-based drives for a variety of payloads and at different operation speeds. Simulation and parameter identification results using the classical Coulomb model, the Coulomb model with Stribeck friction, and the Armstrong model with Stribeck friction, rising static friction, and frictional memory are compared with experimental data, showcasing the effectiveness of the presented approach toward framing the concepts of friction and motion transmission into a robotics setting. [ABSTRACT FROM AUTHOR]

In this article, a novel approach for the versatile wrinkle-free application of (curved) precut adhesive tapes on freeform 3-D surfaces is presented. Straight and curved tape application paths are mapped onto the 3-D object as geodesics and as lines with imposed geodesic curvature, respectively. The proposed surface-based path following control concept extends the classical path following control by a novel parallel contact frame and a parallel projection operator. Using a static state feedback, the robotic system is transformed into a system with linear input-output behavior in the path coordinates. This allows to traverse a path on a 3-D object with a draping roll without turning around the surface normal vector. The latter prevents distortions and wrinkles of the applied tape. Experimental results with a Kuka LBR iiwa 14 R820 demonstrate the feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

This article introduces the analysis and control of a meso-scale two degree-of-freedom robotic endoscopic tool body for minimally invasive surgeries. The design of the robotic tool uses two types of a tendon-driven joint known as a bending flexure joint that allows us to control each degree-of-freedom by minimizing interjoint coupling by design. Pure kinematic modeling and control for these robots may not provide precise control performance due to kinematic uncertainties arising from tendon elongation, tendon slacking, gear backlash, etc. We propose a static model for each of the joints of the robotic tool that avoids several of these problems. Depending on the direction of tendon tension application, the proximal joint displays considerable hysteresis due to the superelastic material characteristics and this is included in our static model. The statics of a highly compliant distal joint is also modeled and validated using finite element analysis and experimental data. Using these models, we develop a control system that comprises of a disturbance observer and the proposed static model to provide precise force control and compensate for joint hysteresis. [ABSTRACT FROM AUTHOR]

A key challenge in robotics is to create efficient methods for grasping objects with diverse shapes, sizes, poses, and properties. Grasping with hand-like end effectors often requires careful selection of hand orientation and finger placement. Here, we present a fingerless soft gripper capable of efficiently generating multiple grasping modes. It is based on a soft, cylindrical accordion structure containing coupled, parallel fluidic channels, which are controlled via pressure supplied from a single fluidic port. Inflation opens the gripper orifice for enveloping an object, while deflation allows it to produce grasping forces. The interior is patterned with a gecko-like skin that increases friction, enabling the gripper to lift objects weighing up to 20 N. Our design ensures that fragile objects, such as eggs, can be safely handled, by virtue of a wall buckling mechanism. In reverse, the gripper can be deflated to reach into an opening or orifice then inflated to grasp objects with handles or cavities. The gripper may also integrate a lip that enables it to form a seal and, upon inflating, to generate suction for lifting objects with flat surfaces. In this article, we describe the design and fabrication of this device and present an analytical model of its behavior when operated from a single fluidic port. In experiments, we demonstrate its ability to grasp diverse objects, and show that its performance is well described by our model. Our findings show how a fingerless soft gripper can efficiently perform a variety of grasping operations. Such devices could improve the ability of robotic systems to meet applications in areas of great economic and societal importance. [ABSTRACT FROM AUTHOR]

The collinear Mecanum drive (CMD) is a novel robot locomotion system, capable of generating omnidirectional motion while simultaneously dynamically balancing, achieved using a collinear arrangement of three or more Mecanum wheels. The CMD has a significantly thinner ground footprint than existing omnidirectional locomotion methods, which does not need to be enlarged with increasing robot height as to avoid toppling during acceleration or external disturbance. This combination of omnidirectional manoeuvrability and a thin ground footprint allows for the creation of tall robots that are able to navigate through much narrower gaps between obstacles than existing omnidirectional locomotion methods. This allows for greater manoeuvrability in confined and cluttered environments, such as that encountered in the personal service and automated warehousing robotics sectors. This article derives the kinematics and dynamics models of the CMD, analyzes controllability and accessibility, and determines the degree to which a CMD can be linearized by feedback. A partial feedback linearization is then performed, and three practically useful nonlinear controllers are derived using a backstepping design approach, all with convergence and stability guarantees for the fully coupled nonlinear model. These are demonstrated both in simulation and on a real-world CMD experimental prototype. [ABSTRACT FROM AUTHOR]

Kinodynamic rapidly-exploring random tree (RRT) planners are effective tools for finding feasible trajectories in many classes of robotic systems. However, they are hard to apply to systems with closed-kinematic chains, like parallel robots, collaborative arms manipulating an object, or legged robots keeping their feet in contact with the environment. The state space of such systems is an implicitly-defined manifold that complicates the design of the sampling and steering procedures, and leads to trajectories that drift from the manifold if standard integration methods are used. To address these issues, this article presents a kinodynamic RRT planner that constructs an atlas of the state space incrementally, and uses this atlas to generate random states, and to dynamically steer the system toward such states. The steering method exploits the atlas charts to compute locally optimal controls based on linear quadratic regulators. The atlas also allows the integration of the equations of motion using local coordinates, which eliminates any drift from the state space manifold and results in accurate trajectories. To the best of our knowledge, this is the first kinodynamic planner that explicitly takes closed kinematic chains into account. In this article, we illustrate the planner performance in significantly complex tasks involving planar and spatial robots that have to lift or throw a load using torque-limited actuators. [ABSTRACT FROM AUTHOR]

In this article, we present a new procedure for the derivation of the linearized kinematics and dynamics of a flexible industrial robotic manipulator. We introduce the Lie groups of dual rotation and dual homogeneous transformation matrices, which are the basis for the derivation of dual twists. In addition, dual screws and dual screw transformation matrices are introduced, which are used for the development of a general and systematic linearization procedure for both kinematics and dynamics. This leads to expressions that are linearized in all the states associated with the elastic motion. The dynamic modeling procedure is based on Kane's method, where the partial velocities and partial angular velocities are given as dual screws arranged as columns of dual projection matrices. The elasticity of the links is modeled by the assumed mode method. The presented procedure is implemented numerically for a 4-degrees of freedom manipulator and the simulation results are given. [ABSTRACT FROM AUTHOR]

This article describes motion planning networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems.MPNet uses neural networks to learn general near-optimal heuristics for path planning in seen and unseen environments. It takes environment information such as raw point cloud from depth sensors, as well as a robot's initial and desired goal configurations and recursively calls itself to bidirectionally generate connectable paths. In addition to finding directly connectable and near-optimal paths in a single pass, we show that worst-case theoretical guarantees can be proven if we merge this neural network strategy with classical sample-based planners in a hybrid approach while still retaining significant computational and optimality improvements. To train the MPNet models, we present an active continual learning approach that enables MPNet to learn from streaming data and actively ask for expert demonstrations when needed, drastically reducing data for training. We validate MPNet against gold-standard and state-of-the-art planning methods in a variety of problems from two-dimensional to seven-dimensional robot configuration spaces in challenging and cluttered environments, with results showing significant and consistently stronger performance metrics, and motivating neural planning in general as a modern strategy for solving motion planning problems efficiently. [ABSTRACT FROM AUTHOR]

In this article, we study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose $\mathtt {AdaSearch}$ , a general algorithm for adaptive source seeking in the face of heterogeneous background noise. $\mathtt {AdaSearch}$ combines global trajectory planning with principled confidence intervals in order to concentrate measurements in promising regions while guaranteeing sufficient coverage of the entire area. Theoretical analysis shows that $\mathtt {AdaSearch}$ confers gains over a uniform sampling strategy when the distribution of background signals is highly variable. Simulation experiments demonstrate that when applied to the problem of radioactive source-seeking, $\mathtt {AdaSearch}$ outperforms both uniform sampling and a receding time horizon information-maximization approach based on the current literature. We also demonstrate $\mathtt {AdaSearch}$ in hardware, providing further evidence of its potential for real-time implementation. [ABSTRACT FROM AUTHOR]

In this article, we are interested in robotic visual object classification using a deep convolutional neural network (DCNN) classifier. We show that the correlation coefficient of the automatically learned DCNN features of two object images carries robust information on their similarity, and can be utilized to significantly improve the robot's classification accuracy, without additional training. More specifically, we first probabilistically analyze how the feature correlation carries vital similarity information and build a correlation-based Markov random field (CoMRF) for joint object labeling. Given query and motion budgets, we then propose an optimization framework to plan the robot's query and path based on our CoMRF. This gives the robot a new way to optimally decide which object sites to move close to for better sensing and for which objects to ask a remote human for help with classification, which considerably improves the overall classification. We extensively evaluate our proposed approach on two large datasets (e.g., drone imagery and indoor scenes) and several real-world robotic experiments. The results show that our proposed approach significantly outperforms the benchmarks. [ABSTRACT FROM AUTHOR]

The 17 papers in this special section focus on resilience in networked robotic systems. This collection of articles aims to provide a deeper understanding of resilience as it pertains to multirobot systems, and to disseminate the current advances in designing and operating networked robotic systems. We understand resilience to be a characteristic that enables amultirobot system to withstand or overcome unexpected adverse conditions or shocks, and unknown, unmodeled disturbances. It refers to the contingent nature of the robots' behaviors that is aimed at preserving their functionality or minimizing the time periods during which their functionality is compromised. The papers explore new algorithmic and mathematical foundations toward resilience. [ABSTRACT FROM AUTHOR]

This article addresses fully distributed multirobot (multivehicle) simultaneous localization and mapping (SLAM). More specifically, a multivehicle scenario is considered, wherein a team of vehicles explore the scene of interest in order to cooperatively construct the map of the environment by locally updating and exchanging map information in a neighborwise fashion. To this end, a random-set-based local SLAM approach is undertaken at each vehicle by regarding the map as a random finite set and updating the first-order moment, called probability hypothesis density (PHD), of its multiobject density. Consensus on map PHDs is adopted in order to spread the map information through the team of vehicles also taking into account the different and time-varying fields of view of the team members. The convergence of the consensus strategy is analyzed theoretically, and the effectiveness of the proposed approach is assessed on both simulated and experimental datasets. The complexity and scalability of the proposed approach are also analyzed both theoretically and experimentally. [ABSTRACT FROM AUTHOR]