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The consensus problem is relevant to different areas ranging from biology, social psychology, and physics to power systems and robotics. Two crucial aspects of the design of a consensus system are the implementation issues that arise in densely connected networks and the presence of malicious agents that try to cause a deviation from a synchronization state. In this article, we introduce a formulation to design the topology of a consensus network to improve its resilience to attacks while remaining sparse and consistent with the a priori structural relations between the agents. Through mathematical analysis and simulations on artificial and real-world cases, we show the benefits and usefulness of using this strategy to design resilient and structurally sparse consensus networks.

Summary Surgeons and their patients recognise that one of the major advances in surgical technique over the last 20 years has been the growth of minimal access surgery by means of laparoscopic and robotic approaches. Partnerships with industry have facilitated the development of advanced technical instruments, light sources, recording devices and optics which are almost out of date by the time they are introduced to surgical practice. However, lest we think that technological innovation is entirely a modern concept, we should remember that our predecessors were masters of their craft and able to apply new technologies to surgical practice. The history of minimal access surgery can be traced back to approximately 5000 years ago and this review aims to remind us of the achievements of historical doctors and engineers, as well as bring more modern developments to wider attention. This review will comprise a three-part series: Part I 3000BC to 1850 Early instruments for viewing body cavities Part II 1850 to 1990 Technological developments Part III 1990 to present Organisational issues and the rise of the robots

Soft robots can provide advantages for medical interventions given their low cost and their ability to change shape and safely apply forces to tissue. This article explores the potential for their use for endoscopically-guided balloon dilation procedures in the airways. A scalable robot design based on balloon catheter technology is proposed, which is composed of five balloons together with a tip-mounted camera and LED. Its design parameters are optimized with respect to the clinical requirements associated with balloon dilation procedures in the trachea and bronchi. Possessing a lumen to allow for respiration and powered by the pressure and vacuum sources found in a clinical procedure room, the robot is teleoperated through the airways using a game controller and real-time video from the tip-mounted camera. The robot design includes proximal and distal bracing balloons that expand radially to produce traction forces. The distal bracing balloon is also used to perform balloon dilation. Three actuation balloons, located between the bracing balloons, produce elongation and bending of the robot body to enable locomotion and turning. An analysis of the actuation balloons, which incorporate helical coils to prevent radial collapse, provides design formulas by relating geometric parameters to such performance criteria as maximum change in actuator length and maximum robot bending angle. Experimental evaluation of a prototype robot inside rigid plastic tubes and

In recent era, IoT-assisted robotics (IoRT) plays a major part in collaborating IoT sensor nodes, robots and human beings to establish a collaborative communication. In this paper, we develop an IoRT system with a modern architecture that encompasses the integration of IoT sensor nodes and robots in smart cities. The study develops a cloud platform to access smart vehicle mobility data in smart cities without collision. A framework is then developed to assist the robots and IoT sensor node in managing and making quick decisions on traffic collision. Hence, it is developed to connect, share, and exchange the information between the cloud and the IoT nodes/robots. The experimental validation shows improved traffic control and collision free traffic in smart cities than other smart city traffic management mechanism.

Multigoal reinforcement learning (RL) extends the typical RL with goal-conditional value functions and policies. One efficient multigoal RL algorithm is the hindsight experience replay (HER). By treating a hindsight goal from failed experiences as the original goal, HER enables the agent to receive rewards frequently. However, a key assumption of HER is that the hindsight goals do not change the likelihood of the sampled transitions and trajectories used in training, which is not the fact according to our analysis. More specifically, we show that using hindsight goals changes such a likelihood and results in a biased learning objective for multigoal RL. We analyze the hindsight bias due to this use of hindsight goals and propose the bias-corrected HER (BHER), an efficient algorithm that corrects the hindsight bias in training. We further show that BHER outperforms several state-of-the-art multigoal RL approaches in challenging robotics tasks.

Background Inguinal lymph node dissection (ILND) is an essential step in both treatment and staging of several malignancies including penile and vulvar cancers. Various open, video endoscopic, and robotic-assisted techniques have been utilized so far. In this review, we aim to describe available minimally invasive surgical approaches for ILND, and review their outcomes and complications. Methods The PubMed, Wiley Online Library, and Science Direct databases were reviewed in February 2020 to find relevant studies published in English within 2000–2020. Findings There are different minimally invasive platforms available to accomplish dissection of inguinal nodes without jeopardizing oncological results while minimizing postoperative complications. Video Endoscopic Inguinal Lymphadenectomy and Robotic Video Endoscopic Inguinal Lymphadenectomy are safe and achieve the same nodal yield, a surrogate metric for oncological adequacy. When compared to open technique, Video Endoscopic Inguinal Lymphadenectomy and Robotic Video Endoscopic Inguinal Lymphadenectomy may offer faster postoperative recovery and fewer postoperative complications including wound dehiscence, necrosis, and infection. The relatively high rate and severity of postoperative complications hinders utilization of recommended ILND for oncologic indications. Minimally invasive approaches, using laparoscopic or robotic-assisted platforms, show some promise in reducing the morbidity of this procedure while achieving adequate short and intermediate term oncological outcomes.

A 69-year-old man was referred to the hepatobiliary surgeons for mild enlargement of an asymptomatic cystic liver lesion found on routine screening in 2017 that measured 3.7×3.6×4.3 cm. Work-up with MRI revealed a complex multilocular cyst that had enlarged to 6.6×5.5×4.6 cm. Other work-up was unremarkable. He had a Eastern Cooperative Oncology Group score of 1; therefore, a surgical excision was planned due to the possibility for malignancy. A robotic approach with enucleation of the lesion was undertaken, with plans for return for a wider resection if pathological examination revealed malignancy. The lesion was noted to be a biliary adenofibroma, an exceptionally rare lesion that is thought to be benign, but requires excision due to potential malignant degeneration. The patient was discharged home the following day and has had minimal pain in his postoperative course.

This paper discusses the digital automation workflows and co-design methods that made possible the comprehensive robotic prefabrication of the BUGA Wood Pavilion - a large-scale production case study of robotic timber construction. Latest research in architectural robotics often focuses on the advancement of singular aspects of integrated digital fabrication and computational design techniques. Few researchers discuss how a multitude of different robotic processes can come together into seamless, collaborative robotic fabrication workflows and how a high level of interaction within larger teams of computational design and robotic fabrication experts can be achieved. It will be increasingly important to discuss suitable methods for the management of robotics and computational design in construction for the successful implementation of robotic fabrication systems in the context of the industry. We present here how a co-design approach enabled the organization of computational design decisions in reciprocal feedback with the fabrication planning, simulation and robotic code generation. We demonstrate how this approach can implement direct and curated reciprocal feedback between all planning domains - paving the way for fast-paced integrative project development. Furthermore, we discuss how the modularization of computational routines simplify the management and computational control of complex robotic construction efforts on a per-project basis and open the door for the flexible reutilization of developed digital technologies across projects and building systems., European Regional Development Fund, Deutsche Forschungsgemeinschaft, Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg, Projekt DEAL, Bundesgartenschau Heilbronn 2019 GmbH, GETTYLAB

Hand detection is a crucial technology for space human-robot interaction (SHRI), and the awareness of hand identities is particularly critical. However, most advanced works have three limitations: 1) the low detection accuracy of small-size objects; 2) insufficient temporal feature modeling between frames in videos; and 3) the inability of real-time detection. In the article, a temporal detector (called TA-RSSD) is proposed based on the SSD and spatiotemporal long short-term memory (ST-LSTM) for real-time detection in SHRI applications. Next, based on the online tubelet analysis, a real-time identity-awareness module is designed for multiple hand object identification. Several notable properties are described as follows: 1) the hybrid structure of the Resnet-101 and the SSD improves the detection accuracy of small objects; 2) three-level feature pyramidal structure retains rich semantic information without losing detailed information; 3) a group of the redesigned temporal attentional LSTM (TA-LSTM) is utilized for three-level feature map modeling, which effectively achieves background suppression and scale suppression; 4) low-level attention maps are used to eliminate in-class similarity between hand objects, which improves the accuracy of identity awareness; and 5) a novel association training scheme enhances the temporal coherence between frames. The proposed model is evaluated on the SHRI-VID dataset (collected according to the task requirements), the AU-AIR dataset, and the ImageNet-VID benchmark. Extensive ablation studies and comparisons on detection and identity-awareness capacities show the superiority of the proposed model. Finally, a set of actual testing is conducted on a space robot, and the results show that the proposed model achieves a real-time speed and high accuracy.

A major challenge for online and data-driven model learning in robotics is the high sample-complexity. This hinders its efficiency and practical feasibility for lifelong learning, in particular for developmental robots that autonomously bootstrap their sensorimotor skills in an open-ended environment. In this work, we propose new methods to mediate this problem in order to permit the learning of robot models online, from scratch, and in a learning while behaving fashion. Exploration is utilized and autonomously driven by a novel intrinsic motivation signal which combines knowledge-based and competence-based elements and surpasses other state-of-art methods. In addition, we propose an Episodic Online Mental Replay to accelerate the online learning, to ensure the sample-efficiency, and to update the model online rapidly. The efficiency as well as the applicability of our methods are demonstrated with a physical 7-DoF Baxter manipulator. We show that our learning schemes are able to drastically reduce the sample complexity and learn the data-driven model online, even within a limited time frame.

Background Robot-assisted laparoscopic prostatectomy (RALP) is a favored surgical approach for treating prostate cancer. However, RALP does not decrease postoperative pain significantly despite its minimal invasiveness. The pain associated with robot-assisted surgery is most severe during the immediate postoperative period. We aimed to demonstrate that preoperative rectus sheath block (RSB) can reduce acute pain after RALP. Methods A prospective non-randomized study with two parallel groups was performed from June 2020 to August 2020. A total of 100 patients undergoing RALP were divided into two groups: the RSB group (n = 50) and the non-RSB group (n = 50). Ultrasound-guided RSB was performed preoperatively only in the RSB group. The primary outcome of the study was the visual analog scale (VAS) pain score during coughing (VAS-C) 1 h after surgery. In addition, the VAS pain score at rest (VAS-R) and the VAS-C were assessed up to 24 h after surgery. The doses of postoperative opioids consumed were also recorded. Results The RSB group had a significantly lower VAS-C 1 h after RALP (58 [47–73] vs. 74 [63–83] mm, p = 0.001). In addition, the RSB group had significantly lower VAS-R and VAS-C scores, and postoperative opioid requirement, up to 6 h after surgery compared to the non-RSB group. Moreover, the VAS-R was significantly lower in the RSB group than in the non-RSB group 24 h after surgery. Conclusion Preoperative RSB significantly improved analgesia during the early period after RALP. The long-term analgesic efficacy of RSB needs further study.

This study aims to explore the usability of the virtual robotics programming curriculum (VRP-C) for robotics programming teaching. Pre-service computer science (CS) teachers were trained for robotics programming teaching by using VRP-C in a scientific education activity. After training, views of the participants were revealed by using a scale and an evaluation form consisting of open-ended questions. Results show that VRP-C is compatible with the curriculum for robotics programming teaching in schools, and pre-service CS teachers tend to use VRP-C in their courses. They think that VRP-C will be beneficial for robotics programming teaching in terms of content, functionality, and cost. Compatibility, visual design, feedback, time management, fiction, gamification, and cost are the characteristics that increase the usability of VRP-C. VRP-C can be used as an online tool for robotics programming training due to the necessity of transition to distance education because of the COVID-19 pandemic.

The developments in sensor technology, information processing, computer science, and artificial intelligence significantly improved robots’ autonomy. Robots’ external perception relies on sensing technology. Thus, capturing accurate sensor information is vital for ensuring robotic security and improving human-machine interaction performance. This paper classifies the main robotic sensors, describes multi-sensor information fusion and processing and contrasts the state-of-the-art sensor technologies for collaborative robots with other state-of-the-art technologies in related fields. In addition, this paper also introduces collaborative robots’ perception applications of the state-of-the-art representative products, the new designs for collaborative robots, the interactive applications of the intelligent Kinect sensor with collaborative robots, and the important applications of collaborative robots in the medical fields. Through a deep analysis of relevant information, this paper aims to introduce the integration of the state-of-the-art sensor technologies and collaborative robots, with hoping of guiding significance for the applications of robot sensors. This paper finally emphasizes the sensors’ impact on robot performance and discusses future research on sensor technologies in robotics.

Robots are a combination of mechatronics, computer science, and artificial intelligence. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of actions. Whenever a robot has to interact with the human-society, it has to adopt a special skill called Human-Robot Interaction and thus the term Social-Robot comes into account. A social robot has to be able to express emotions, communicate with high-level dialogue, use natural cues, and learn to recognize models of other agents. An autonomous social robot cannot follow orders instead of doing something on its own. To make the robot more interactive and communicative, lots of sensors and modules have to be used along with its moving mechanism. Therefore, a social robot becomes complex and expensive. To overcome the issue of the complexity and costliness, in this paper, a design of social robot using a combination of embedded systems, the Internet of Robotic Things (IORT) and Android operating system has been introduced to be interactive and communicative to human, be intelligent enough to solve complex mathematics and be able to follow the operator’s command simultaneously. By using the Internet as the robot’s source of information and the Android phone as the robot’s sensory and control system partially, and adding them all to the robot’s embedded system wirelessly, we have not only become able to make the robot more advanced and intelligent, but also reduce the cost of construction by a significant amount.

BACKGROUND Chronic urinary retention (CUR) is a frequent complication after orthotopic neobladder (ONB) reconstruction in women. To decrease CUR, several open surgical modifications to provide back support to the ONB have been established on the basis of pelvic anatomical differences between females and males. OBJECTIVE To illustrate our technique for robotic intracorporeal reconfiguration of ONB as integrated into our open surgical approach to provide back support to the ONB with round ligaments in women. DESIGN, SETTING, AND PARTICIPANTS From November 2017 to April 2021, 28 patients underwent robotic intracorporeal ONB with a minimum of 6 mo of follow-up at a single centre. SURGICAL PROCEDURE We performed robotic radical cystectomy, pelvic lymphadenectomy, and a complete intracorporeal ONB suspended with round ligaments (rONB). Our surgical procedure is demonstrated in the accompanying video. MEASUREMENTS Demographics and clinical and pathological data were collected. Perioperative and 90-d complications and 6-mo functional outcomes were compared for the rONB group (n = 12) and the patients receiving a traditional ONB (tONB; n = 16). RESULTS AND LIMITATIONS The median total operative time was 305 min (interquartile range [IQR] 270-370) for tONB and 303 min (IQR 287-330) for rONB. The median estimated blood loss was 325 ml (IQR 200-700) for tONB and 350 ml (IQR 262-600) for rONB. Some 50% of the tONB group and 41.7% of the rONB group experienced low-grade complications. A total of 12.5% tONB and 8.3% rONB patients experienced high-grade complications with neobladder-vaginal fistula. The cumulative risk of CUR was 37.5% in the tONB group and 16.7% in the rONB group. This study is limited by the small sample size and the short follow-up period. CONCLUSIONS We established a feasible surgical technique for a robotic intracorporeal ONB configuration suspended with round ligaments. This may prevent the occurrence of emptying dysfunction in women. PATIENT SUMMARY We describe our stepwise technique for creating a new bladder within the body that is suspended with round ligaments. Patients undergoing removal of the bladder for bladder cancer may benefit from this technique in terms of better urinary function and the advantages of a robotic surgical approach.

Monocular 3D object detection is a low cost and challenging task for autonomous vehicles and robotics. Utilizing a monocular image for 3D object detection is served as an auxiliary module for autonomous vehicles and is a growing concern recently. Currently, the expensive lidar and stereo cameras have a predominant performance on accurate 3D object detection, whereas monocular based methods are considerably lower in performance. This performance gap is minimized by reforming the monocular based method as a single internal network. We exploit the correlation between 2D and 3D detection spaces, enabling 3D boxes to leverage feature maps generated in image space. The 2D and 3D proposals are extracted through a proposal generation network that is enhanced and utilized for estimating accurate 3D detection and localization. Experimental results on the KITTI dataset demonstrate that in comparison to other monocular object detection methods the proposed method considerably improved the accuracy of 3D object detection. The mean average precision of 3D object detection in front view is improved to 25% and the bird's eye view to 32% for the car class on the moderate difficulty level.

Background Focal therapy (FT) for prostate cancer is less invasive than radical treatment but carries a risk of recurrence. Salvage robot-assisted radical prostatectomy (S-RARP) is a possible option after FT failure. Objective To evaluate the impact of FT on functional and oncological outcomes following S-RARP. Design, setting, and participants In a retrospective analysis of data from a prospectively collected institutional database, 53 patients who underwent S-RARP following failure of focal ablation were selected as group I; patients who had whole-gland ablation and external beam therapy were excluded. This group was matched to a control sample (matched at ratios of 1:1, 1:2, 1:3, 1:4) of men who had undergone primary RARP, using age, prostate-specific antigen (PSA), PSA density, body mass index, Sexual Health Inventory for Men score, American Urological Association symptom score, Charlson comorbidity index, prostate weight, preoperative Gleason score (GS), and history of smoking as variables. Surgical procedure S-RARP after FT was performed using a standardized technique developed at our institute with the da Vinci Xi Surgical System. Measurements Oncological and functional outcomes were compared between the S-RARP and primary RARP groups. Results and limitations There was no difference in estimated blood loss (p = 0.8) between the 1:1 matched groups, but operating room time was significantly longer for S-RARP (p = 0.007). The primary RARP group had a higher proportion of patients who underwent a full nerve-sparing procedure. The S-RARP group had higher incidence of positive surgical margins (40% vs 15%; p = 0.008), GS ≥8 (25% vs 15%; p = 0.07), and positive lymph node status (9.4% vs 5.7%; p = 0.02). There was no significant difference in overall complications between the groups. The primary RARP group had a higher incidence of lymphocele drainage after surgery (15% vs 0%; p = 0.006). The main limitation of the study is its retrospective design. Conclusions S-RALP after FT failure is feasible; however, surgery following FT leads to poorer oncological and functional outcomes. Despite the targeted nature of FT, significant nonfocal collateral damage is evident in tissues surrounding the prostate, which in turn translates to poorer functional outcomes after S-RARP. Patient summary We studied the surgical challenges during robot-assisted removal of the prostate after previous focal treatment (FT) for prostate cancer and compared the outcomes to those for robot-assisted prostate removal in patients who had no previous FT. We found that this technique is safe and effective with a limited risk of complications, but poor urinary and sexual functional outcomes.

Learning to adapt one’s gait with environmental changes plays an essential role in locomotion of legged robots which remains challenging for constrained computing resources and energy budget, as in the case of edge-robots. Recent advances in bio-inspired vision with dynamic vision sensors (DVS) and associated neuromorphic processing can provide promising solutions for end-to-end sensing, cognition and control tasks. However, such bio-mimetic closed-loop robotic systems based on event-based visual sensing and actuation in the form of spiking neural networks (SNN) have not been well explored. In this work, we program the weights of a bio-mimetic multi-gait central pattern generator (CPG) and couple it with DVS-based visual data processing to show a spike-only closed-loop robotic system for a prey-tracking scenario. We first propose a supervised learning rule based on stochastic weight updates to produce a multi-gait producing Spiking-CPG (SCPG) for hexapod robot locomotion. We then actuate the SCPG to seamlessly transition between the gaits for a nearest prey tracking task by incorporating SNN based visual processing for input event-data generated by the DVS. This for the first time, demonstrates the natural coupling of event data flow from event-camera through SNN and neuromorphic locomotion. Thus, we exploit bio-mimetic dynamics and energy advantages of spike-based processing for autonomous edge-robotics.

Space robotics has been used extensively in complex space missions. Rigid-manipulator space robots may suffer from rigid-body collisions with targets. This collision is likely to cause damage to the space robot and the target. To overcome such a problem, a novel Continuum-Manipulator Space Robot (CMSR) for performing on-orbit servicing missions is proposed in this paper. Compared with rigid-manipulator space robots, CMSRs are able to perform compliant operations and avoid rigid-body collisions with a target. The CMSR consists of two kinds of flexible components, including solar arrays and continuum manipulators. The elastic vibrations of these flexible components disturb the position and attitude of CMSRs. The beating phenomenon introduced by the energy transfer among these flexible components can cause damage to solar arrays. The complicated dynamic coupling poses enormous challenges in dynamic modeling and vibration analysis. The dynamic model for CMSRs is derived and the mechanism of the beating phenomenon is analyzed in this paper. Simulation results show that an obvious beating phenomenon occurs and the amplitude of the solar arrays increases significantly when the natural frequencies of two kinds of flexible components are close. A method is provided to avoid the beating phenomenon.

Personalized mobile healthcare integrated with various wearable devices has become a significant area of interest in the present era. In the current research work, a flexible, wearable and disposable paper-based continuous skin temperature monitoring sensor for early medical prognosis and accurate diagnosis of body temperature-related ailments, such as COVID-19, is proposed. Conventional screen-printing and drop-casting techniques were used to fabricate the proposed sensor using MWCNTs as the sensing material and paper as the substrate. The linearity, stability, repeatability and durability of the sensors were tested from 29°C (room temperature) to 60°C. A thin sheet of PET was laminated over the sensor surface to ascertain its stability toward environmental effects and physical movements, and a response time of ~13 s and a recovery time of ~38 s with a sensitivity of -0.0685% °C-1 were recorded. The efficacy of the proposed sensor was ascertained by placing it at different body locations on a human subject and comparing it with a standard thermocouple and IR sensor. The sensor even helped to effectively distinguish minimal temperature variations between various regions of the body. Furthermore, the feasibility of the fabricated temperature sensor as a temperature-based tactile sensor for robotics/artificial skin applications and as a noncontact breath monitoring device for use in personalized healthcare monitoring applications was investigated.

Farms and mines facing a shortage of workers willing to do dangerous or low-paying jobs are turning to cutting-edge robotics, cloud computing, and artificial intelligence (AI) to leapfrog ahead of ...

Tunable lens technology inspired by the human eye has opened a new paradigm of smart optical devices for a variety of applications due to unique characteristics such as lightweight, low cost, and facile fabrication over conventional lens assemblies. The fast-growing demands for tunable optical lenses in consumer electronics, medical diagnostics, and optical communications require the lens to have a large focal length modulation range and high compactness. Herein, for the first time, an all-solid tunable soft lens driven by highly transparent dielectric elastomer actuators (DEAs) based on poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) and waterborne polyurethane (PEDOT:PSS/WPU) transparent electrodes is developed. The deformation of the tunable soft lens is achieved by the actuation of DEAs, mimicking the change of the surface profile of the human eye to achieve remarkable focal length variations. Upon electrical activation, this tunable soft lens can vary its original focal length by 209%, which is one of the highest among current tunable soft lenses and far beyond that of the human eye. This study demonstrates that transparent DEAs are capable of achieving focus-variation functions, and potentially useful in artificial robotic vision, visual prostheses, and adjustable glasses, which will induce significant effects on the future development of tunable optics.

Research on soft artificial muscles (SAMs) is rapidly growing, both in developing new actuation ideas and improving existing structures with multifunctionality. The human body has more than 600 muscles that drive organs and joints to achieve desired functions. Inspired by the human muscles, this article presents a new type of SAM fiber formed from twisting and braiding soft hydraulic filament artificial muscles with high aspect ratio, high strain, and high energy efficiency. We systematically investigated the relationship between input pressure and output elongation as well as contraction force of the new muscles using different configurations in terms of an array of single and multiple muscles arranged in nontwisting (or straight), twisting, and braiding variants. Experimental results revealed that the twisting and braiding configurations greatly enhanced the muscle elongation and generated force compared with their nontwisting/braiding counterparts. To demonstrate the new muscles' usability, we implemented several muscle variants to bidirectionally manipulate 3D-printed human fingers and elbow, mimicking the human upper limb with a full range of motion. We also created a bioinspired growing soft tubular muscle that could simultaneously exert longitudinal and radial expansion upon pressurization, similar to that of auxetic metamaterial structures. The new growing soft tubular muscles were experimentally validated and the results showed that they could be potentially implemented in several emerging applications, including smart compression garments, stent-like supporting devices, and tubular grippers for medical use.

In robotics, decoding complex human movements of multiple degrees of freedom (DOFs) from surface electromyography (sEMG) remains challenging. Recently, the rapid development of artificial intelligence (AI) technology provides a new solution to this problem. In this paper, we propose an AI-based framework that consists of a series of deep learning approaches, for achieving a precise and robust decoding on three-dimensional (3D) wrist movements. A previously developed device (WMD) was utilized to tag the myoelectric signals with 3D kinematic labels. A public dataset (HIT-SimCo) was established, wherein the sEMG signals were collected from diverse wrist movements and multiple subjects. A lightweight convolutional neural network (CNN) was constructed, which can extract the motion-related features directly from raw sEMG, and make motion predictions in an end-to-end manner. In addition, several data augmentation strategies were explored to improve the robustness of the model against environmental variations; and a fine-tuning policy was proposed to further improve the subject-specific accuracy. The decoding model was finally tested in three scenarios: a robotic arm/hand system performing daily-living tasks (pouring, screwing, etc.), a supernumerary robotic hand playing blocks-building, and a virtual prosthesis conducting motor rehabilitation training.

Localization is an essential technique in mobile robotics. In a complex environment, it is necessary to fuse different localization modules to obtain more robust results, in which the error model plays a paramount role. However, exteroceptive sensor-based odometries(ESOs), such as LiDAR/visual odometry, often deliver results with scene-related error, which is difficult to model accurately. To address this problem, this research designs a scene-aware error model for ESO, based on which a multimodal localization fusion framework is developed. In addition, an end-to-end learning method is proposed to train this error model using sparse global poses such as results from global positioning system(GPS) and inertial measurement unit(IMU). The proposed method is realized for error modeling of LiDAR/visual odometry, and the results are fused with dead reckoning to examine the performance of vehicle localization. Experiments are conducted using both simulation and real-world data of experienced and unexperienced environments, and the experimental results demonstrate that with the learned scene-aware error models, vehicle localization accuracy can be largely improved and shows adaptiveness in unexperienced scenes.

In this study, we present a new experiment in order to study the Lombard effect in telepresence robotics. In this experiment, one person talks with a robot controled remotely by someone in a different room. The remote pilot (R) is immersed in both environments, while the local interlocutor (L) interacts directly with the robot. In this context, the position of the noise source, in the remote or in the local room, may modify the subjects’ voice adaptations. In order to study in details this phenomenon, we propose four particular conditions: no added noise, noise in room R heard only by R, virtual noise in room L heard only by R, and noise in room L heard by both R and L. We measured the variations of maximum intensity in order to quantify the Lombard effect. Our results show that there is indeed a modification of voice intensity in all noisy conditions. However, the amplitude of this modification varies depending on the condition.

Hardware platforms for socially interactive robotics can be limited by cost or lack of functionality. This paper presents the overall system -- design, hardware, and software -- for Quori, a novel, affordable, socially interactive humanoid robot platform for facilitating non-contact 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., 20 pages. 21 figures. This was accepted to and will be published to the IEEE Transactions on Robotics Journal

This work presents a unique approach to the design, fabrication, and characterization of paper-based origami robotic systems consisting of stackable pneumatic actuators. These paper-based actuators (PBAs) use materials with high elastic modulus-to-mass ratios, accordion-like structures, and direct coupling with pneumatic pressure for extension and bending. The study contributes to the scientific and engineering understanding of foldable components under applied pneumatic pressure by constructing stretchable and flexible structures with intrinsically nonstretchable materials. Experiments showed that a PBA possesses a power-to-mass ratio greater than 80 W/kg, which is more than four times that of human muscle. This work also illustrates the stackability and functionality of PBAs by two prototypes: a parallel manipulator and a legged locomotor. The manipulator consisting of an array of PBAs can bend in a specific direction with the corresponding actuator inflated. In addition, the stacked actuators in the manipulator can rotate in opposite directions to compensate for relative rotation at the ends of each actuator to work in parallel and manipulate the platform. The locomotor rotates the PBAs to apply and release contact between the feet and the ground. Furthermore, a numerical model developed in this work predicts the mechanical performance of these inflatable actuators as a function of dimensional specifications and folding patterns. Overall, we use stacked origami actuators to implement functionalities of manipulation, gripping, and locomotion as conventional robotic systems. Future origami robots made of paper-like materials may be suitable for single use in contaminated or unstructured environments or low-cost educational materials.

An innovative and flexible approach is introduced to address the challenge of self-organizing a group of mobile robots into cubic-spline based patterns without any requirement of control points. Besides the self-organization of mobile robots, the approach incorporates a potential field-based control for obstacle/collision avoidance. This will offer more flexibility to swarm robots to efficiently dealing with many practical situations including smoothly avoiding obstacles during movement, or exploring and covering areas with complex curved patterns. Essentially, this challenge is approached by proposing a formation control model basing on a Smoothed Particle Hydrodynamic estimation technique, which uses special cubic-spline kernel functions applied here to interpolate the density of each robot in the swarm. The moving information is used to weight the distances to the robot’s neighbours available in its field of view. Then an artificial physics mesh is finally built among each robot and its three available neighbours having the smallest weighted distances. Significant results toward emerging cubic-spline patterns are shown with a swarm of foot-bot mobile robots simulated in the ARGoS platform. Analysis results with different metrics are also conducted to assess the performance of the model with different swarm sizes and in the presence of sensory noise as well in the presence of partially faulty robots.

A modular actuator construction consisting of smaller articulating units in series was designed to construct soft pneumatic actuators. These units are constructed to have a preferential bending direction using Mold Star 15 and Smooth-Sil 950 silicone. By changing the orientation of each unit, a different deformed actuator shape can be achieved. A design tool was developed where a genetic algorithm was coupled with a nonlinear finite element solver. This design tool optimizes the design using the genetic information available in the initial population over multiple generations and presents a candidate design that best resembles a desired profile specified as the objective function. A two-dimensional reduced order model was developed that reduces the time for each function evaluation from ≈20 min for a three-dimensional (3D) numerical analysis to ≈45 s. The design tool was tasked to solve design targets ranging from sine and cosine functions of various amplitudes to final actuator tip positions. In each case, the inflated actuator resembled the desired profile. Selected physical actuators were cast and tested. 3D scanning was used to capture the inflated shape and compared it to the numerical solution. A quantitative comparison showed a maximum average deviation of2.5% of the uninflated actuator length between the physical and numerical models. The proposed design tool proved successful in designing shape matching actuators with close agreement to physical models.

Background Robotic distal pancreatectomy has been accepted to be safe and effective for pancreatic tail lesion. Whether spleen preservation by preserving the splenic vessels with robot assistance is feasible and beneficial remains controversial. Here we would like to compare the operative outcomes of robotic distal pancreatectomy and splenectomy (DPS) with robotic spleen preserving distal pancreatectomy by means of splenic vessel preservation (SVP). Methods Between March 2011 and September 2019, 56 consecutive patients undergoing robotic distal pancreatectomy were identified, with 28 patients in each group. Patient demographics, histopathology findings and operative outcomes were prospectively collected and compared between the two groups. A subgroup analysis was made after excluding malignant and pancreatic lesions >6 cm in the DPS group. Results The two groups had similar conversion rate, blood loss, morbidity and pancreatic fistula rate. There was no operative mortality. The SVP group had shorter median operative time (245 vs 303.5 min, P = 0.019) and shorter median hospital stay (5 vs 6 days, P = 0.019) than the DPS group. However, all malignant lesions occurred in the DPS group and lesion size in DPS group was significantly larger. After matching, there were 28 SVP and 15 DPS. The histopathology findings and lesion size became comparable. The SVP group still had shorter operative time (245 vs 290 min, P = 0.022) and shorter hospital stay (5 vs 7 days, P = 0.014) than the DPS group. Conclusion Apart from avoiding risk of overwhelming postsplenectomy sepsis, robotic SVP had additional advantage of shorter operative time and shorter hospital stay than robotic DPS.

Biorobotics is increasingly attracting engineers worldwide, due to the high impact this field can have on the society. Biorobotics aims at imitating or taking inspiration from mechanisms and strategies evolved by animals, including their locomotion abilities in real scenarios, such as swimming, running, crawling, and flying. However, the development of skin-mimicking structures, allowing protection without hindering artifacts' movements, has been rarely addressed. Skin-mimicking structures play a key role for biomimetic robots that have to move in unstructured environments. Currently most of the skin used for robots in engineering adopts soft materials or bellow structures to enable both structural deformation and protection. However, the elastic nature of the former can produce support failure and increasing strain with deformation, while the humpy surface of the latter reduces the interactive performance with the environment. Herein, we designed a novel compliant structure for biorobots' skin, fabricated through a special configuration of both soft and rigid materials to reproduce attributes provided by natural epithelial structures. The presented skin has a simple fabrication process, as well as it is cost effective. The structure of this skin includes a thin conical shape where rigid iron rings are wrapped by soft polyester fabrics, allowing a theoretically zero elastic modulus when bended and stretched. The dimension of fabrics was specified to allow rigid rings having a certain range of free rotation and translation. The possibility of free bending and stretching of the structure was implemented by overcoming low sliding friction of adjacent rings. To empirically test the effectiveness of the proposed structure, a model, including 20 segments, was also fabricated. Experimental results from the bending tests, both in aerial and underwater environments, as well as from the folding tests, demonstrated the successful performance of the skin prototype in terms of low resistance and energy consumption. Finally, the proposed highly compliant structural skin was mounted and tested on a fish robot previously developed by authors, to further show its effectiveness.

Soft gripping provides the potential for high performance in challenging tasks through morphological computing; however, design explorations are limited by a combination of a difficulty in generating useful models and use of laborious fabrication techniques. We focus on a class of grippers based on granular jamming that are particularly difficult to model and introduce a "one shot" technique that exploits multimaterial three-dimensional (3D) printing to create entire grippers, including membrane and grains, in a single print run. This technique fully supports the de facto physical generate-and-test methodology used for this class of grippers, as entire design iterations can be fitted onto a single print bed and fabricated from Computer-Aided Design (CAD) files in a matter of hours. Initial results demonstrate the approach by rapidly prototyping in materio solutions for two challenging problems in unconventional design spaces; a twisting gripper that uses programmed deformations to reliably pick a coin, and a multifunctional legged robot paw that offers the ability for compliant locomotion over rough terrains, as well as being able to pick objects in cluttered natural environments. The technique also allows us to easily characterize the design space of multimaterial printed jamming grippers and provide some useful design rules. The simplicity of our technique encourages and facilitates creativity and innovation. As such, we see our approach as an enabling tool to make informed principled forays into unconventional design spaces and support the creation of a new breed of novel soft actuators.

The optimal minimally invasive surgical approach to mediastinal tumors is unknown. There are limited reports comparing the outcomes of resection with robotic-assisted thoracoscopic surgery (RATS) and video-assisted thoracoscopic surgery (VATS) surgery. We hypothesized that patients who underwent RATS would have improved outcomes.The National Cancer Database was queried for all patients who underwent a minimally invasive surgical approach for any mediastinal tumor from 2010 to 2016. Patients were determined to have an adverse composite outcome if they had any of the adverse perioperative outcomes: conversion to open procedure, 90-day mortality, 30-day readmission, and positive pathologic margins. Secondary outcomes of interest were length of stay and overall survival. Multivariable logistic regression was used to assess likelihood of having a composite adverse outcome based on surgical approach.The study included 856 patients: 402 (47%) underwent VATS and 454 (53%) underwent RATS. RATS resections were associated with fewer conversions (4.9% vs 14.7%, P.001), fewer positive margins (24.3% vs 31.6%, P = .02), shorter length of stay (3.8 days vs 4.3 days, P = .01), and fewer composite adverse events (36.7% vs 51.3%, P.001). Multivariate analysis showed RATS (odds ratio, 0.44; P.001) was independently associated with a decreased likelihood of a composite adverse outcome, even among tumors exceeding 4 cm (odds ratio, 0.45; P = .001). Overall survival was similar between the 2 groups.Among patients who underwent a minimally invasive surgical approach for a mediastinal tumor, RATS had fewer adverse outcomes than VATS, even for tumors 4 cm or larger. These data suggests that RATS may be the preferred technique for patients who are candidates for minimally invasive resection of mediastinal tumors.

Geometrically multifunctional structures inspired by nature can address the challenges in the development of soft robotics. A bioinspired structure based on origami and kirigami can significantly enhance the stretchability and reliability of soft robots. This study proposes a novel structure with individual, overlapping units, similar to snake scales that can be used to construct shape-morphing batteries for untethered soft robots. The structure is created by folding well-defined, two-dimensional patterns with cutouts. The folding lines mimic the hinge structure of snakeskin, enabling stable deformations without mechanical damage to rigid cells. The structure realizes multi-axial deformability and a zero Poisson's ratio without off-axis distortion to the loading axis. Moreover, to maximize areal density, the optimal cell shape is designed as a hexagon. The structure is applied to a stretchable Li-ion battery, constructed to form an arrangement of electrically interconnected, hexagonal pouch cells.

Purpose To report the results of a first-in-human study using a robotic device to assist subretinal drug delivery in patients undergoing vitreoretinal surgery for macular haemorrhage. Design Double armed, randomized controlled surgical trial (ClinicalTrials.gov identifier: NCT03052881). Methods Setting Oxford Eye Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom. Participants In total, 12 participants were recruited: 6 in the robot-assisted and 6 in the control manual surgery arm according to the pre-specified inclusion and exclusion criteria. All subjects presented with acute loss of vision due to a sub-foveal haemorrhage secondary to neovascular age-related macular degeneration. Intervention After standard vitrectomy, intraoperative OCT-guided subretinal injection of tissue plasminogen activator (TPA) was performed either by robot-assisted or conventional manual technique under local anaesthesia. The robotic part of the procedure involved advancement of a cannula through the retina and stabilizing it during foot-controlled injection of up to 100µl of TPA solution. Main Outcome Measures We assessed surgical success, duration of surgery, adverse events, and tolerability of surgery under local anaesthesia. Results The procedure was well tolerated by all participants and safely performed in all cases. Total duration of surgery, time taken to complete the injection and retinal microtrauma were similar between the groups and not clinically significant. Subretinal haemorrhage was successfully displaced at 1-month post-intervention, except for one control subject, and the median gain in visual acuity was similar in both arms. Conclusions This first-in-human study demonstrates the feasibility and safety of high precision robot-assisted subretinal drug delivery as part of the surgical management of sub-macular haemorrhage, simulating its potential future application in gene or cell therapy.

Background: Retroperitoneal approach and segmental renal artery clamping in partial nephrectomy are techniques that facilitate postoperative recovery and renal function preservation. This study aimed to compare the renal function preservation and perioperative outcomes of robot-assisted partial nephrectomy (RAPN) and laparoscopic partial nephrectomy (LPN) with these techniques. Materials and Methods: Clinical parameters of 43 patients who had undergone retroperitoneal RAPN from March 2017 to December 2019 were retrospectively collected and compared with those of 52 patients who had undergone retroperitoneal LPN at the same period in our institution. Differences in operating time, warm ischemia time, estimated blood loss, complications, postoperative hospital stay, as well as renal function loss were compared between the two groups. Results: Background characteristics between RAPN and LPN groups such as age, gender, BMI, and tumor characteristics were comparable. All RAPNs and LPNs were successfully completed without conversion to open surgery or nephrectomy. No significant difference in operating time, estimated blood loss, complications, and postoperative hospital stay was observed between RAPN and LPN groups. The warm ischemia time in RAPN group was slightly shorter than that of LPN groups (P = .054). Compared with the LPN group, the RAPN group was significantly associated with less glomerular filtration rate reduction and renal volume loss rate (P = .042 and P = .013, respectively). Conclusions: The perioperative outcomes were comparable between the two groups. However, compared with LPN, RAPN had superiority in preserving renal function in our series.

Modern industrial robots can work alongside human workers and coordinate with other robots. This means they can perform complex tasks, but doing so requires complex programming. Therefore, robots are typically programmed by experts, but there are not enough to meet the growing demand for robots. To reduce the need for experts, researchers have tried to make robot programming accessible to factory workers without programming experience. However, none of that previous work supports coordinating multiple robot arms that work on the same task. In this paper we present four block-based programming language designs that enable end-users to program two-armed robots. We analyze the benefits and trade-offs of each design on expressiveness and user cognition, and evaluate the designs based on a survey of 273 professional participants of whom 110 had no previous programming experience. We further present an interactive experiment based on a prototype implementation of the design we deem best. This experiment confirmed that novices can successfully use our prototype to complete realistic robotics tasks. This work contributes to making coordinated programming of robots accessible to end-users. It further explores how visual programming elements can make traditionally challenging programming tasks more beginner-friendly.

Currently, the Newton-Raphson iterative algorithm has been extensively employed in the fields of basic research and engineering. However, when noise components exist in a system, its performance is largely affected. To remedy shortcomings that the conventional computing methods have encountered in a noisy workspace, a novel modified Newton integration (MNI) algorithm is proposed in this article. In addition, the steady-state error of the proposed MNI algorithm is smaller than that of the Newton-Raphson algorithm under a noise-free or noisy workspace. To lay the foundations for the corresponding theoretical analyses, the proposed MNI algorithm is first converted into a homogeneous linear equation with a residual term. Then, the related theoretical analyses are carried out, which indicate that the MNI algorithm possesses noise-tolerance ability under various noisy environments. Finally, multiple computer simulations and physical experiments on robot control applications are performed to verify the feasibility and advantage of the proposed MNI algorithm.

LunAres Research Station is an analog research station for crewed space mission simulation, located at the abandoned airport in Poland. The facility provides full isolation, allowing for complex research on the psychological and physiological impact of long-term extra-terrestrial human presence. The general objective of LunAres is to create a research platform to support scientific and technological development in human space exploration. A broad range of specialists is involved in the study from fields like extreme medicine, psychology, biotechnology, robotics and engineering, sociology, architecture. The possible observation and control of the indoor environment, as well as telemetry of the crew’s physical and psychological states, provide large quantities of data for complex studies This paper presents an overview of the LunAres Research Station activity since its establishment in 2017. The contribution to scientific research and technology development is included. Based on the presented experience of carried missions and crews’ feedback a roadmap regarding the station design advancement is determined. The decision-making process of development is presented finalized with the demonstration of the potential for future projects and studies regarding human missions and sustainability. The conclusions of upgraded functional plans and spaces were determined through research on existing references and the new strategy for LunAres. Detailed drawings regarding architectural and technical solutions as well as future steps will be introduced.

Objective To automate surgeon skills evaluation using robotic instrument kinematic data. Additionally, to implement an unsupervised mislabeling detection algorithm to identify potentially mislabeled samples that can be removed to improve model performance. Methods Video recordings and instrument kinematic data were derived from suturing exercises completed on the Mimic FlexVR robotic simulator. A structured human consensus-building process was developed to determine Robotic Anastomosis Competency Evaluation technical scores across 3 human graders. A 2-layer long short-term memory–based classification model used instrument kinematic data to automate suturing skills assessment. An unsupervised label analyzer (NoiseRank) was used to identify potential mislabeling of skills data. Performance of the long short-term memory model’s technical skill score prediction was measured by best area under the curve over the training runs. NoiseRank outputted a ranked list of rated skills assessments based on likelihood of mislabeling. Results 22 surgeons performed 226 suturing attempts, which were broken down into 1,404 individual skill assessment points. Automation of needle entry angle, needle driving, and needle withdrawal technical skill scores performed better (area under the curve 0.698–0.705) than needle positioning (0.532) at baseline using all available data. Potential mislabels were subsequently identified by NoiseRank and removed, improving model performance across all domains (area under the curve 0.551–0.766). Conclusion Using ground truth labels from human graders and robotic instrument kinematic data, machine learning models have automated assessment of detailed suturing technical skills with good performance. Further, an unsupervised mislabeling detection algorithm projected mislabeled data, allowing for their removal and subsequent improvement of model performance.

Background: We aim to directly compare the feasibility and safety of extended pelvic lymph node dissection (PLND) during transperitoneal robotic-assisted radical prostatectomy (Tp-RARP) and extraperitoneal laparoscopic radical prostatectomy (Ep-LRP). Materials and Methods: We retrospectively identified the prospectively maintained database records of 162 patients diagnosed with prostate cancer (PC) who underwent Ep-LRP or Tp-RARP with extended PLND. Patients with risk of nodal metastases over 5% according to Briganti nomogram received extended PLND. All data analyzed in this study were based on the documentation in our PC database including age, body mass index, Charlson comorbidity index score, preoperative prostate-specific antigen, history of abdominal surgery, biopsy Gleason score, total operation time, postoperative pelvic drainage time, pathological results, lymph node yield (LNY), percentage lymph node involvement (%LNI), and perioperative complications. Patients were followed up for biochemical recurrence in the postoperative period. Results: Eighty-two of the 162 enrolled patients were in group 1 (Ep-LRP+PLND) and 80 were in group 2 (Tp-RARP+PLND). There were no statistically significant differences between the groups regarding preoperative demographics and clinical characteristics. The median LNY was 17 (range 8-27) and 17.5 (range 10-29) in groups 1 and 2, respectively, and no statistically significant difference was found. There was no significant difference between the groups in terms of biochemical recurrence-free survival with mean follow-up of 44.8 months after radical surgery. Conclusion: Our results support the view that extended PLND through the Ep-LRP approach is a feasible and safe procedure without compromising oncological efficacy compared with a similar template attempted during Tp-RARP. Clinical Trial Registration number is 01/21-2.

The grand challenge toward the miniaturization of untethered robots is the lack of a proper actuation system that can effectively transform the limited onboard energy into an untethered movement of the whole body. Here, in this article, we present an insect scale, 5-mm-long, and 9.4 mg in body mass untethered crawling robot powered by a 37 mg onboard ceramic capacitor. With high electromechanical efficiency by utilizing self-excited electrostatic vibration, the prototype device can be directly powered by an onboard capacitor without any boost or frequency conversion circuitry, and forward movements have been achieved at an average moving speed of 5.9 mm/s for 1.68 s. The maneuverability of locomotion from solid ground to water and different moving directional controls via the leg designs have been demonstrated for the advancements in the field of millimeter-scale robotics research.

This paper proposes a scheme of space vector optimization for model predictive control (MPC) of dual three-phase permanent magnet synchronous machine (DTP-PMSM), which aims to restrain the current harmonics under the condition of low inductance. The multiphase electric motor possesses the characteristics of quick current variation rate by means of the low inductance. Especially incorporating with MPC schemes, its advantage of quick response shows promising prospects for various applications such as robotics, aerospace and medical devices. However, such a characteristic of low inductance requires shorter control period, which means that the traditional MPC cannot be implemented to suppress the current harmonics since the performance of the power switching devices and the digital controller limits the arbitrary increasing of the control frequency. This paper presents the space-vector-optimized model predictive control (SVO-MPC) for DTP-PMSMs with low inductance by pre-synthesizing space vectors to eliminate harmonics and optimizing the zero vector to deal with quick current response. As a result, the proposed SVO-MPC can remarkably improve the steady and dynamic control performance, while the traditional MPC almost fails at commonly-adopted control frequency. Lastly, simulated and experimental results are both given to verify the feasibility of the proposed SVO-MPC for DTP-PMSMs with quick current response.

One of the most effective ways to improve quality of life in dementia is by exposing people to meaningful activities. The study of engagement is crucial to identify which activities are significant for persons with dementia and customize them. Previous work has mainly focused on developing assessment tools and the only available model of engagement for people with dementia focused on factors influencing engagement or influenced by engagement. This article focuses on the internal functioning of engagement and presents the development and testing of a model specifying the components of engagement, their measures, and the relationships they entertain. We collected behavioral and physiological data while participants with dementia (N = 14) were involved in six sessions of play, three of game-based cognitive stimulation and three of robot-based free play. We tested the concurrent validity of the measures employed to gauge engagement and ran factorial analysis and Structural Equation Modeling to determine whether the components of engagement and their relationships were those hypothesized. The model we constructed, which we call the ENGAGE-DEM, achieved excellent goodness of fit and can be considered a scaffold to the development of affective computing frameworks for measuring engagement online and offline, especially in HCI and HRI.

Motion planning in unstructured and low-speed environments is a fundamental and difficult task for all mobile robotics. If we view motion planning as an optimization problem, the non-convex collision avoidance constraints and the nonlinear vehicle dynamic constraints make motion planning challenging and time-consuming. In this paper, we propose a Successive Linearization in Feasible Set (SLiFS) algorithm to address these two difficulties. SLiFS consists of two steps. The first step is to iteratively construct convex feasible sets around the current trajectory to approximate non-convex collision avoidance constraints. The second step is to successively linearize the nonlinear dynamic constraints along the current trajectory and further penalize them into the objective function to avoid infeasible linearized constraints, so that the current trajectory can be reshaped within the obtained convex feasible sets by iteratively solving the linearized optimization problem. The main innovation of SLiFS algorithm is that we consider the L₁ norm type penalty function in the second step. We find that the sparsity of the L₁ norm might help to satisfy the robotic dynamic constraints by numerical experiments. Numerical testing results show that our proposed SLiFS algorithm has a high success rate to find feasible trajectories and costs much less time than the classical interior-point method.

Rapid development in robotics and bionics make it possible for robotic leg prostheses to help amputees while impose challenges on duplicating the motion characteristics of amputees' healthy leg. One critical problem in prosthetic control is joint angle drift problem, that is, a repetitive motion trajectory in task space cannot guarantee that the generated motion trajectories in joint space are also repetitive. In order to solve the problem, we propose neural-dynamics optimization for robotic leg prostheses to generate the repetitive joint trajectories in real-time. Our proposed method duplicates the self-selected walking speed of the amputees' healthy leg. The online motion generation is formulated as a constrained quadratic programming (QP) optimization problem whose objective function adopts the kinetic energy and joint displacement performance criterion. The varying parameter convergent differential neural network is developed as a real-time QP solver which can globally converge to the optimal solution of the constrained QP problem. Then, a repetitive learning controller is designed for robotic leg prostheses to reduce the tracking errors while following the repetitive motion. Through the physical experiments on the developed two-degree-of-freedom robotic leg prosthesis worn on an amputee, the neural-dynamics optimization is substantiated to be a remedy of online motion generation, and the effectiveness of repetitive learning control for reducing the prosthetic tacking errors is verified.

Universal grasping for a diverse range of objects is a challenging problem in robotics, especially in the presence of mixed properties with fragile/rigid and heavy/light. Toward universal grasping, this article presents a practical and systematic grasping control framework that enables a variable stiffness gripper to handle the objects with diverse properties using a category-aware force regulation approach, termed classification-based force grasping. Under this framework, a convolutional neural network (CNN) is employed to classify the category of the grasping object, and a grasping force is determined based on the classified category through a database that records a predefined force magnitude per category. Sequentially, the gripper can be adjusted to a force-optimized stiffness, which facilitates the achievement of an accurate grasping force regulation in a large range. Technically, two novel enabling modules are developed for grasping classification and execution, respectively. First, a novel weight imprinting technique based on center-guided feature embedding is proposed for object classification. It enables the CNN to efficiently handle novel object categories using only a few samples even without retraining/fine-tuning. Second, a vision-based grasping force sensing module is developed, which takes advantage of the specifically designed variable-stiffness gripper. Its grasping force can be estimated from the deflection angle of finger flexure by the vision so that the contact force can be sensed and regulated. Remarkably, only single-source vision information is needed for both of the above modules without any additional force sensor. Experiments are conducted extensively to evaluate the performance of the proposed force grasping approach.

In this paper, we present a novel robotic system for skin photo-rejuvenation procedures, which can uniformly deliver the laser's energy over the skin of the face. The robotised procedure is performed by a manipulator whose end-effector is instrumented with a depth sensor, a thermal camera, and a cosmetic laser generator. To plan the heat stimulating trajectories for the laser, the system computes the surface model of the face and segments it into seven regions that are automatically filled with laser shots. We report experimental results with human subjects to validate the performance of the system. To the best of the author's knowledge, this is the first time that facial skin rejuvenation has been automated by robot manipulators., Comment: 11 pages, 16 figures