In this paper, an adaptive force-motion control framework with orientation compliance is present for redundant manipulators in physical interaction with unknown surfaces. The proposed framework includes control task space definition and double-closed-loop control based on external force loop approach. Firstly, a specification matrix is designed merely through force feedback to ensure the control task space defined in orthogonal spaces. Then, an orientation compliance controller and a force-motion close-loop controller are constructed in the outer-loop control of external force feedback loop approach. Secondly, the output of outer-loop control task, along with boundary constraints and optimization indexes is formulated as a nolinear dynamic programming problem. Next a finite-time convergence neural network based inner-loop controller is proposed for this category of dynamic programming problem and its stability and convergence analysis are given. Simulations verify the convergence and effectiveness of the proposed framework. The real-world experiments show that the Mean Integral of the Absolute Error of the proposed control framework is reduced by 77.26% compared with constant impedance control.

In this paper, a fixed-time adaptive deferred constrained control strategy is proposed for a flexible single-link manipulator system with input saturation. Fuzzy Neural Networks are utilized to estimate the unknown dynamics of the flexible manipulator system as well as the errors caused by input saturation. To address output constraints imposed within a prescribed time period, a time-shift function and an adjusted barrier function are introduced. The system’s stability is rigorously proven using the direct Lyapunov method. Finally, numerical simulations and experimental results are presented to validate the effectiveness and superiority of the proposed control approach.

Visual Simultaneous Localization and Mapping (VSLAM) is a critical technology for autonomous driving and mobile robotics. Traditional VSLAM methods based on discrete representations, such as point clouds, offer high computational efficiency and excellent localization accuracy, but they exhibit limited robustness. In contrast, methods employing field representations, like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3D GS), provide greater robustness at the expense of increased computational demands and reduced localization accuracy. Hybrid VSLAM approaches that attempt to combine these representations typically rely on serial, synchronous cascades, which compromise robustness, computational efficiency, and GPU memory usage. This paper introduces a novel adaptive cross-representation VSLAM framework that applies different representation modeling techniques to distinct regions of an image sequence and adopts asynchronous parallel modeling in overlapping regions. A Vision Language Model (VLM) is used to analyze the image sequence, enabling the detection of representation modeling regions and adaptive switching between representations. Cross-representation data association is performed through a coarse-to-fine feature selection process, resulting in a globally consistent map. The proposed method is evaluated on both public and custom-collected datasets, where experimental results show that it surpasses state-of-the-art methods in terms of robustness, computational efficiency, localization accuracy, and GPU memory usage.

A framework is proposed in this paper for learning variable impedance in percutaneous puncture surgery, with the aim of simplifying the robotic puncture of soft tissues. The framework involves simulating the dynamic changes that occur when the human arm interacts with human tissues and transferring the resulting adaptive capabilities to the robot through learning movement trends and stiffness changes. To enhance performance during task execution, we integrate the variable impedance control framework with the interactive operation and feedback controllers. To provide flexibility for trajectory modification during operation, derivative Gaussian processes are introduced to identify the target position and obtain a model of motion trends. This control law is combined with virtual dynamics that describe puncture dynamics, enabling the robot to regulate interactions and plan its trajectory. We present experiments involving tissue puncturing tasks performed by the Franka-Emika Panda robot with varying degrees of hardness. The results demonstrate that our framework is capable of learning manipulation skills for physical interaction with humans, thereby reducing application complexity in tasks involving complex force interactions for robots. Compared to using fixed or variable impedance gain controllers, our approach effectively improves the success rate, stability, and efficiency of percutaneous puncture. Note to Practitioners—This paper is motivated by the limitations encountered by robots when handling deformed objects. In traditional robot control processes, the assumption of a fixed and unchanging contact object poses a significant challenge in applying robot control to the medical industry. Consequently, it becomes imperative for robot control systems to develop stable intelligent approaches capable of interacting with deformed objects. In this paper, we propose a framework for robot-assisted puncture that combines robotic impedance control techniques with sensing mechanisms. By integrating these approaches, our framework demonstrates effectiveness in performing tasks involving soft tissues with varying levels of hardness. Our proposed method encompasses three main ideas: 1) Sensing muscle activity during task execution enables the acquisition of task parameters from the human arm. 2) The utilization of a robot control method enhances the stability of the robot’s execution process. 3) The proposed method shows potential for application in processing and treating objects with low stiffness, deformed objects, and thin-walled parts. Experimental results validate the effectiveness of the developed method. In future work, it is important for the robot-assisted puncture system to consider recognizing and localizing more diverse targets to enhance its generalization capabilities.

This study presents an adaptive event-triggered control methodology for flexible manipulator systems with prescribed performance and input backlash. To reduce the communication burden between the controllers and actuators, we consider a relative threshold event-triggered mechanism. Then, an adaptive inverse function is applied to eliminate the input backlash of the actuator, and a neural network is adopted to handle the system uncertainty. It is proven that the proposed control approach not only ensures the tracking error converges to a small region close to zero within the prescribed time but also significantly reduces overshoot by using Lyapunov’s direct method. Furthermore, the efficacy of the scheme proposed is demonstrated through numerical simulations and experiments.

Distributed k-winners-takes-all (k-WTA) neural network (k-WTANN) models have better scalability than centralized ones. In this work, a distributed k-WTANN model with a simple structure is designed for the efficient selection of k winners among a group of more than k agents via competition based on their inputs. Unlike an existing distributed k-WTANN model, the proposed model does not rely on consensus filters, and only has one state variable. We prove that under mild conditions, the proposed distributed k-WTANN model has global asymptotic convergence. The theoretical conclusions are validated via numerical examples, which also show that our model is of better convergence speed than the existing distributed k-WTANN model.

Stroke survivors usually have dyskinesia, who have an urgent need for rehabilitation-assist training. To reduce the labor of rehabilitation therapists, this paper attempts to investigate an effective rehabilitation-assisted robot skill acquisition framework, which is inspired by the scheme of robot learning from demonstration (LfD). Since most of the current LfD methods were implemented with rigorous assumptions that the considering motion features are only represented on an individual manifold. Meanwhile, despite many advancements that have been achieved on time-position trajectories and position-velocity trajectories, those methods are restricted to Euclidean space and can not be applied to learn those dexterous and compliant rehabilitation-assisted robot skills such as position-orientation trajectories and force-stiffness trajectories, etc. In this paper, we propose a novel skill acquisition framework for rehabilitation-assisted robot using manifold-mappings and Gaussian processes, which allows the robot to 1) simultaneously considering the robot position, orientation, force as well as stiffness by manifold-mappings among d-dimensional Euclidean space$\mathcal{R}^{d}$, special orthogonal group$S\mathcal{O}$(3), and Riemannian space$\mathcal{M}$, respectively, which resulting in accurate motion and compliant behavior; 2) retrieving skill representation by encap-sulating the variability of multiple high-dimensional demon-strations that with input-dependent noises; 3) implementing the via-points-based trajectory modulation by considering task constraints or environmental changes. To simplify the writing, we named the proposed framework as Multi-motion Features Fusion-based Robot Skill Learning (MF2RoSL). To effectively evaluate the effectiveness of our proposed method, an upper limb rehabilitation training system with a collaborative Kinova robot is developed. The training exercises of our system are determined according to the Brunnstrom therapeutic approach to the management of hemiplegic patients, including the 3-DoFs movement of the shoulder joint and a 7-DoF movement of an insertion/extraction task for assessing the activities of daily living (ADL). Results indicate that our proposed MF2RoSL method allows the robot to learn rehabilitation skills from the therapist and can be rapidly adapted to new patients.

Robot Learning from humans is a promising paradigm for directly transferring human skills to robots. This learning allows robots to encapsulate task constraints and motion patterns from human demonstrations as well as acquire skills that can be adapted to unseen scenarios. Even though many state-of-the-art skill-learning successes have been achieved, simultaneously addressing variability from a complex and long-horizon manipulation task and generalizing it to external uncertainty remains a challenge. This efficient skill learning has to allow for handling large-scale, high-dimensional demonstrations, adapting to environmental changes (starting, via and end points and obstacles), generalizing to task constraints (trajectory precision, stiffness), and measuring uncertainty in the reproduction. To this effect, we present a novel robot skill-learning framework called SVGP-CoGP that will implement all the aforementioned properties by encoding task variability from multiple demonstrations using Sparse Variational Gaussian Processes (SVGP) and adapting to additional constraints via a coregionalized multi-output GP (CoGP) based on SVGP. The proposed method can significantly reduce the computational complexity of model fitting by making use of the variational inference of GP models, which makes it possible for robots to learn skills from complex and long-horizon tasks. We evaluated and compared the effectiveness and strengths of our framework with existing probabilistic methods on a Kinova robot that performed emergency button-pressing tasks. The results indicated that our framework allowed the robot to learn skills from complex and long-horizon manipulation tasks that outperformed baselines both in quantitative evaluation and in an online test. Note to Practitioners–The objective of this work is to address the problem of robot-learning from complex and long-horizon manipulation tasks to allow end-users to teach robots new tasks by having them learn from human demonstrations instead of being programmed. We start with a brief historical overview of widely used methods and summarize five prominent capabilities that a skill-learning approach should have: variability, uncertainty, correlation, extrapolation, and adaptability. We then propose an entirely GP-based skill-learning framework by simultaneously addressing all those capabilities by using a sparse variational Gaussian process (SVGP) in conjunction with a coregionalized multioutput GP model. The proposed framework incorporated variational inference and kernel treatments such that the robot learned skills from large-scale demonstrations and high-dimensional trajectories. Finally, experimental evaluation and performance comparisons were performed in a real robot button-pressing task, the results of which indicated that our proposed method enables robots to achieve complex and long-horizon manipulation tasks in dynamic and unstructured environments. With the rapid development of collaborative robots in service and industry, our findings have application scenarios as diverse as robot learning from demonstration, robot skill learning, human–robot collaboration, and other complex and long-horizon manipulation tasks.

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collision-avoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collision-avoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.

A novel LED backlight device is introduced in this paper, which is mainly used for transparent or translucent glass bottle detection relevant to machine vision. A special controlling and drive circuit is adopted in this LED backlight device to realize independent control of each color and rapid and convenient change of many colors and luminance. Because the area of LED backlight can be changed at random according to the size of tested product, many application requirements will be met easily. On the other hand, safe and effective cooling equipment is used to improve stability of the whole system.

Combining the advantages of active and passive walking robots, we have developed a novel active biped walking robot with only six DoFs. The robot is built with six 1-DoF joint modules and two wheels as the feet. It achieves locomotion in special gaits different from those of traditional biped robots. In this paper, this novel biped robot is introduced, and four walking gaits, namely turning-around gait, foot-wheel hybrid gait, side-stepping gait and turning-over gait are proposed, and their walking patterns and motion planning are presented and analyzed. Walking experiments are carried out to verify the locomotion function, the effectiveness of the presented gaits and to illustrate the features of this novel biped robot. It has been shown that biped active walking may be achieved with only a few DoFs and simple kinematic configuration.

High-rise tasks in agriculture, forestry and building industry requires robots possessing climbing function. Motivated by these potential applications and inspired by the climbing motion of animals such as inchworms, we have developed a novel biped climbing robot - Climbot. Built with a modular approach, the robot consists of five 1-DoF joint modules connected in series and two special grippers mounted at the ends. With this configuration, Climbot is able not only to climb a variety of media, but also to grasp and manipulate objects, and hence is a ¿mobile¿ manipulator. In this paper, we first introduce the development of this novel robot, and then illustrate three climbing gaits based on the unique configuration of the robot. Experiments of climbing poles are carried out to verify the climbing functions and to demonstrate potential application of the proposed robot.

Modules have been widely used in the development of re-configurable robots and snake-like robots. Modular methodology can also be applied in design of other robots. To build robots flexibly and quickly with low costs, we have developed two basic joint modules and several functional modules including grippers, suckers and wheels/feet as end-effectors. In this paper, we introduce the development of these modules, and present several novel robots built using them. Specifically, we show how to use them to set up a manipulator, a 6-DoF biped walking robot, a wheeled mobile robot, a biped tree-climbing robot, and a biped wall-climbing robot. It has been shown that a few modules can easily spawn a variety of novel robots with modular methodology.

UNLABELLED: MicroRNA (miRNAs) play essential roles in post-transcriptional gene regulation in animals and plants. Several existing computational approaches have been developed to complement experimental methods in discovery of miRNAs that express restrictively in specific environmental conditions or cell types. These computational methods require a sufficient number of characterized miRNAs as training samples, and rely on genome annotation to reduce the number of predicted putative miRNAs. However, most sequenced genomes have not been well annotated and many of them have a very few experimentally characterized miRNAs. As a result, the existing methods are not effective or even feasible for identifying miRNAs in these genomes. Aiming at identifying miRNAs from genomes with a few known miRNA and/or little annotation, we propose and develop a novel miRNA prediction method, miRank, based on our new random walks- based ranking algorithm. We first tested our method on Homo sapiens genome; using a very few known human miRNAs as samples, our method achieved a prediction accuracy greater than 95%. We then applied our method to predict 200 miRNAs in Anopheles gambiae, which is the most important vector of malaria in Africa. Our further study showed that 78 out of the 200 putative miRNA precursors encode mature miRNAs that are conserved in at least one other animal species. These conserved putative miRNAs are good candidates for further experimental study to understand malaria infection. AVAILABILITY: MiRank is programmed in Matlab on Windows platform. The source code is available upon request.

MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional gene regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well-understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana, and Oryza sativa. We show that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have. To further characterize the promoters of microRNA genes, we developed a novel promoter prediction method, called common query voting (CoVote), which is more effective than available promoter prediction methods. Using this new method, we identify putative core promoters of most known microRNA genes in the four model species. Moreover, we characterize the promoters of microRNA genes in these four species. We discover many significant, characteristic sequence motifs in these core promoters, several of which match or resemble the known cis-acting elements for transcription initiation. Among these motifs, some are conserved across different species while some are specific to microRNA genes of individual species.